[Date RR # approved – ASTM to assign] · 2013-08-28 · [Date RR # approved – ASTM to assign]...

[Date RR # approved – ASTM to assign]

Committee E08 on Fatigue and Fracture

Subcommittee E08.06 on Crack Growth Behavior

Research Report [RR # – ASTM to assign]

Interlaboratory Study to Establish Precision Statements for ASTM E647, Standard Test Method for Measurement of Fatigue Crack Growth Rates

Prepared by: Peter C. McKeighan James H. Feiger Dustin H. McKnight

ASTM International 100 Barr Harbor Drive

West Conshohocken, PA 19428-2959

ROUND ROBIN TEST PROGRAM AND RESULTS FOR FATIGUE CRACK GROWTH MEASUREMENT

IN SUPPORT OF ASTM STANDARD E647

Prepared by

Peter C. McKeighan James H. Feiger

Dustin H. McKnight

Southwest Research Institute (SwRI®) San Antonio, Texas

Prepared for

ASTM International Committee E08 on Fatigue and Fracture

Subcommittee E08.06 on Crack Growth Behavior West Conshohocken, PA

February 2008

S O U T H W E S T R E S E A R C H I N S T I T U T E® SAN ANTONIO HOUSTON WASHINGTON, DC

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Acknowledgements

This effort would not have been possible without the input from all of the participants in

the round robin test program. The hard work provided gratis from each of the participants and

their labs is greatly appreciated. Furthermore, the United States Air Force is acknowledged for

graciously helping to provide the material for this program. MTS and Instron were kind enough

to supply most of the specimen machining with the remainder supplied by Southwest Research

Institute (SwRI). The Alcoa Technical Center provided machining of the notches in the

aluminum M(T) specimens. Finally, numerous ASTM Committee E08 individuals assisted in

this effort. Over the last decade, as this round robin evolved from idea to reality, excellent

support was consistently provided by John Ruschau (UDRI) and Steve Thompson (AFRL). Kind

acknowledgement is also extended to Ms. Loretta Mesa (SwRI) for preparing this manuscript

and the numerous ASTM members who reviewed this report prior to publication. Thanks to

everyone for making this collaboration a resounding success!

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TABLE OF CONTENTS Section Page Acknowledgements......................................................................................................................... ii Executive Summary ..................................................................................................................... viii 1.0 Introduction.............................................................................................................................1 2.0 Materials and Methods............................................................................................................3 2.1 Materials ........................................................................................................................3 2.2 Round Robin Participants ..............................................................................................4 2.3 Specimen Geometry.......................................................................................................5 2.4 Round Robin Rules ........................................................................................................5 3.0 Analysis Methodology..........................................................................................................13 3.1 Interpolating Crack Growth Rate Data ........................................................................13 3.2 Applying the Interpolation Approach ..........................................................................14 4.0 Results...................................................................................................................................27 4.1 Final Matrix of Testing and Conditions.......................................................................27 4.2 The Big Six ..................................................................................................................28 5.0 Discussion.............................................................................................................................43 5.1 Analysis Methodology.................................................................................................43 5.2 Interpolated Data for Each of the Six Conditions........................................................44 5.3 Interlaboratory Variability ...........................................................................................46 5.4 Intralaboratory Variability ...........................................................................................47 5.5 Variability Summary....................................................................................................47 5.6 Individual Laboratory Performance.............................................................................49 5.7 Statistical Anomalies?..................................................................................................50 5.8 Influence of Test Technique Variables ........................................................................51 6.0 Summarizing and Concluding Remarks ...............................................................................81 7.0 References.............................................................................................................................85 Appendix A – Supporting Guiding (Pretest) Documentation Appendix B – Individual Lab Data Plots

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LIST OF TABLES Table Page 2-1 Materials involved in the FCG round robin............................................................................8 2-2 Tensile properties of the materials involved in the FCG round robin ....................................8 2-3 Round robin participants.........................................................................................................9 2-4 Specimen IDs for different materials and specimen geometries ..........................................10 2-5 Generalized test conditions for the round robin testing including approximate lower

and upper bound envelope of crack growth rates and applied stress intensity factor ranges ....................................................................................................................................10

3-1 Tabulated values of average normalized growth rate (with standard deviation)

interpolated at various normalized ΔK levels .......................................................................16 4-1 Summary of round robin tests for each lab...........................................................................30 4-2 Specimen data summary for the 4130 steel material ............................................................31 4-3 Specimen data summary for the thinner 2024-T351 aluminum material .............................32 4-4 Specimen data summary for thicker 2024-T351 and 7075-T6 aluminum materials ............33 4-5 Primary test conditions for the different labs involved in the round robin...........................34 5-1 Processed statistics for the different labs testing condition STL-A and STL-B ...................53 5-2 Processed statistics for the different labs testing condition ALX-A and ALX-C.................54 5-3 Processed statistics for the different labs testing condition ALX-B and ALN-A.................55 5-4 Laboratory-by-laboratory breakout for testing conditions STL-A and STL-B ....................56 5-5 Laboratory-by-laboratory breakout for testing conditions ALX-A and ALX-C ..................57 5-6 Laboratory-by-laboratory breakout for testing conditions ALX-B and ALN-A ..................58 5-7 Influence of crack length measurement technique (DCPD and compliance) on

overall variability levels........................................................................................................59 5-8 Influence of loading method (constant amplitude versus K-control) on overall

variability levels....................................................................................................................60

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LIST OF FIGURES Figure Page 2-1 Notch requirements for the different specimens in E647-05 (scanned from

standard) ..............................................................................................................................11 2-2 Specimen geometries extracted from E647-05 illustrating specimen dimensions

for both C(T) and M(T) configurations (scanned from standard) .......................................12 3-1 Sample set of fatigue crack growth rate data ......................................................................17 3-2 Sample set of fatigue crack growth rate data with a subdivided ΔK in equal log

space increments (10 per decade)........................................................................................17 3-3 Interpolation strategy employed on either side of a ΔK increment .....................................18 3-4 Interpolation calculations for the data shown previously....................................................19 3-5 All of the data sets plotted on a common set of axes ..........................................................20 3-6 All data sets with the interpolated average da/dN rate at each normalized ΔK level..........21 3-7 All data sets with the interpolated average and ±2 SD’s on FCGR da/dN..........................22 3-8 Typical error bound of the crack growth rate data at a given normalized ΔK ....................23 3-9 Distribution of interpolated growth rates at a given normalized ΔK ..................................24 4-1 Typical data sets from two of the participating laboratories (all data is plotted in

Appendix B) ........................................................................................................................35 4-2 All fatigue crack growth associated with test condition STL-A .........................................36 4-3 All fatigue crack growth associated with test condition STL-B .........................................37 4-4 All fatigue crack growth associated with test condition ALX-A ........................................38 4-5 All fatigue crack growth associated with test condition ALX-C ........................................39 4-6 All fatigue crack growth associated with test condition ALX-B ........................................40 4-7 All fatigue crack growth associated with test condition ALN-A ........................................41

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LIST OF FIGURES (Cont’d) Figure Page 5-1 Schematic approach of how spreadsheet-based analysis is organized ................................61 5-2 Actual portion of the spreadsheet analysis for condition ALN-1........................................62 5-3 Comparing the interpolated data with the actual for condition STL-A in terms of

(a) growth rate and (b) variability .......................................................................................63 5-4 Comparing the interpolated data with the actual for condition STL-B in terms of

(a) growth rate and (b) variability .......................................................................................64 5-5 Comparing the interpolated data with the actual for condition ALX-A in terms of

(a) growth rate and (b) variability .......................................................................................65 5-6 Comparing the interpolated data with the actual for condition ALX-C in terms of

(a) growth rate and (b) variability .......................................................................................66 5-7 Comparing the interpolated data with the actual for condition ALX-B in terms of

(a) growth rate and (b) variability .......................................................................................67 5-8 Comparing the interpolated data with the actual for condition ALN-A in terms of

(a) growth rate and (b) variability .......................................................................................68 5-9 Average interlaboratory variability using (a) range extent ratio and (b) exponent .............69 5-10 Average intralaboratory variability using (a) range extent ratio and (b) exponent .............70 5-11 Relationship between inter- and intralaboratory variability using (a) range extent

ratio and (b) exponent..........................................................................................................71 5-12 Intralaboratory variability for conditions (a) STL-A and (b) STL-B..................................72 5-13 Intralaboratory variability for conditions (a) ALX-A and (b) ALX-C................................73 5-14 Intralaboratory variability for conditions (a) ALX-B and (b) ALN-A................................74 5-15 Laboratory performance for the different conditions (plot shows ranking method) ...........75 5-16 Comparing the interpolated data with the actual for Lab Q data in terms of (a)

growth rate and (b) variability.............................................................................................76

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LIST OF FIGURES (Cont’d) Figure Page 5-17 Anomalous Lab O data in terms of (a) growth rate (compared to interpolated) and

(b) variability .......................................................................................................................77 5-18 More typical Lab L data in terms of (a) growth rate (compared to interpolated) and

(b) variability .......................................................................................................................78 5-19 Effect of (a) crack length measurement method and (b) test control variability.................79

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EXECUTIVE SUMMARY About the same time the ASTM fatigue crack growth standard (E647) was drafted, a round

robin program was held to quantify the variability observed when measuring fatigue crack

growth rate. Over the ensuing 35 years, technology has evolved considerably which may have

an impact on crack growth rate variability. This report details the results from an extensive

round robin performed using three materials (a steel and two structural aluminum alloys) and

encompassing a variety of specimen geometries and load ratios. In total, 141 fatigue crack

growth rate tests were performed during this testing. A systematic method was developed that

allowed quantifying growth rate (and associated variability in growth rate) at specific ΔK levels

for each test. The steel material exhibited the lowest level of interlaboratory (between labs)

variability. In general for steel, the growth rate variability observed is 1.9x whereas for

aluminum it is 2.4x. For 2024 and 7075 (the aluminum alloys utilized during testing), the mean

growth rate variabilities are 2.3x and 2.6x, respectively. The intralaboratory (within a given lab)

variability for steel is 1.5x, whereas for aluminum it is 1.65x. For 2024 and 7075, the mean

growth rate variabiltities are 1.6x and 1.7x, respectively. The interlaboratory variability ranged

from a low of 1.2x to typically 2.5-3.0x for all of the materials considered. The variability data

suggests that there is little statistical difference between that measured today and that

documented 33 years ago in a previous round robin assessment. The data suggests a slight

decrease in intralaboratory variability over this time period. Some influence of specimen

geometry was noted with M(T) specimens exhibiting variability levels that are 30-40% less than

similar C(T) specimens. A comparison between tests performed using DCPD and compliance as

the continuous, non-visual crack length measurement suggests that variability levels are 20% less

for DCPD when compared to compliance. Conversely, no discernable difference in variability

level was noted between different load control methods (constant amplitude versus K-control).

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1.0 INTRODUCTION Thirty-six years ago, shortly after drafting the original version of the fatigue crack growth

(FCG) rate standard (ASTM E647 [1]), a round robin test program was performed to determine

the precision and bias of the test method. Since this time, gradual changes have occurred in the

body of the E647 test standard as a consequence of such things as servohydraulic control

capability improvements, test automation (few crack growth rate tests are performed manually

anymore), K-control test strategies and non-visual crack length technology.

The current ASTM E647-05 fatigue crack growth standard is a mature standard: the

version cited in Reference [1] totals 45 pages and cites 111 external references. However the

standard continues to evolve and embrace new approaches. For instance, a new method for

analyzing closure data (the ACR approach pioneered by Keith Donald at FTA) and a

compression load precracking methodology (spearheaded by Jim Newman at Mississippi State)

are both being drafted for likely inclusion in the standard.

This is not an easy test standard on which to perform round robin testing. Matters are

complicated by the flexibility that the current standard allows. For instance, a myriad of

different paths (i.e. specific test procedures) are allowed to generate FCG data for a given

material. Furthermore, a variety of nonvisual crack length measurement techniques are all

possible. Normally, in a round robin the test method is fixed and the single-valued output is then

assessed for variability. The absence of a single-valued result from FCG testing and the multiple

allowable paths seriously complicate a round robin test program for evaluating the E647

standard. Therefore, the intent of the round robin herein is to use whichever standard technique

desired to generate the FCG data for three materials (2024 aluminum, 7075 aluminum and 4130

steel). Different specimen geometries, specimen sizes and load ratios were perturbed during

testing. This report will describe the testing and the resulting data.

An extensive round robin like the one described in this report takes an enormous amount of

time to plan and execute. The initial discussions raising the possibility of a fatigue crack growth

round robin were held in the mid-1990s at the biannual ASTM committee meetings. Several

informal meetings were held during this period with likely participants working through the


details of how the program would be structured. The strong opinion of the group was that

variability should be measured without confining testing to a strict protocol. Rather, E647

should be used in all its perturbations during the testing so that the result was a true measure of

the variability that can occur with the entire standard.

The material for the program was originally purchased in December 1998. Finding funding

or donations for the machining of test specimens proved to be a challenge to produce the 185

specimens. Letters soliciting testing were sent in October 2003, with specimens finally

completed and distributed to interested parties a year later in October 2004. The data flowed in,

with the first data set received April 2005 and the last data set January 2008.


2.0 MATERIALS AND METHODS The purpose of this section of the report is to discuss the materials utilized for testing, the

specimen designs selected, and the “rules” enforced during testing.

2.1 Materials

When this round robin was first conceived, the organizers felt that it was important to

utilize test materials that were structurally relevant. Since the vast majority of the fatigue crack

growth testing is performed for the aerospace community, aerospace materials were a natural

selection. The organizers did not want to limit the testing to a single type of material, for

instance aluminum. Therefore both aluminum and steel were tested during this program.

The thickness of the test material can have an impact on crack growth rate properties. In

order to achieve a range of behaviors, the following mix of materials was sought:

• Thin high-strength 7075 aluminum (theorized to be best behaved), • Well-behaved, high-strength steel in a medium thickness condition, and

• Thicker lower-strength 2024 aluminum (theorized to be less well behaved due

to the lower strength and expected high levels of crack closure).

The goal with these material-thickness selections was to have available a range of crack

propagation behavior during growth rate measurements for generating the round robin data.

The materials that were utilized were residual stock from past material characterization

programs. More specifically, the material utilized for round robin testing consisted of 4130 steel

bar and sheets of 2024 and 7075 aluminum. A further description of the materials is provided in

Table 2-1. The 4130 steel bar was normalized and heat treated to relatively high strength levels.

It should also be noted that the 36 feet of steel product length was cut into 23 pieces prior to heat

treating. Conversely, the aluminum sheets, each delivered in the temper of interest, consisted of

large, 4’ x 8’ sheets. The thin (1/8-inch thick) 7075-T6 was nominally a sheet product whereas

the thicker (3/8-inch thick) 2024-T351 was nominally classified as a plate product.


All of the materials were supplied with property validation certificates in accordance with

usual practice when testing aerospace materials. Nevertheless, tensile testing was performed on

each of the materials in the longitudinal direction as shown in Table 2-2. Since the steel was

heat treated after cutting, ten of the bars were sampled for property variation. Little variation

was evident with an average ultimate strength of 175 ksi and a yield of 167 ksi. Little variation

was also apparent across the width of the bar as well (note that specimens STRR-1, -2 and -3 in

Table 2-2 were sampled across the width of the bar). Tensile properties for the two aluminum

products are also shown in Table 2-2 and compared with expected results from Reference [2].

Tensile properties were in accordance with expectation and no anomalies were noted during

testing.

2.2 Round Robin Participants

In October of 2003, a letter was mailed to 61 individuals all affiliated in some manner with

testing labs. This letter solicited support and resources for a fatigue crack growth round robin.

These 61 individuals, working for US and foreign entities, were identified from technical

contacts in the fatigue and fracture field. A number of the recipients had already expressed

interest in participating in the test program. The purpose of the letter was to outline the reason

for the round robin and provide a sense of the scope of testing required. One of the key reasons

for the letter was to understand how many specimens the different labs felt that they could test as

well as the relevant specimen geometry that their fixtures allowed.

In the end, 20 test labs volunteered to participate and data was received from the 18

described in Table 2-3. The two labs not able to participate had heavier-than-normal workloads

and could not prioritize jobs that were not revenue-generators. Of the 18 labs (described in

additional detail in Table 2-3) that participated, three were non-US labs (Australia, India and

Canada). The excellent return rate (18 of 20 labs or 90%) was due to the extended time period

given to generate the FCG data. As indicated in Table 2-3, the first lab returned data in April

2005 with the last data sets received at SwRI in January 2008.

In all subsequent tables and data descriptions, the laboratories participating in the round

robin will be identified by a random alphanumeric identifier.


2.3 Specimen Geometry

The two most common fatigue crack growth rate specimen geometries, C(T) and M(T),

were utilized during testing. All specimens were oriented in the L-T direction (implying the

primary loading direction was L with the crack growing in the T direction). Schematics of the

notch and specimen geometries are provided in Figures 2-1 and 2-2 for background. The actual

specimen sizes utilized are indicated in Table 2-4. Considering the data in Table 2-4, two

different sized 4130 steel C(T) specimens were utilized with widths of 2 inch and 3 inch. These

51 specimens were all also notched and the specimen thickness was 0.25 inch.

Fifty-nine similar C(T) specimens were fabricated from the thicker 2024 material as

indicated in Table 2-4. Note that the specimen thickness was reduced to 0.25 inch from the

original product thickness of 0.375 inch. Each of the individual round robin test labs were also

responsible for notching these specimens.

Middle-cracked tension specimens were utilized from full-thickness 2024 and 7075

material as indicated in Table 2-4. Each of these specimens was 4-inch wide and 24-inch long.

The long specimen length was provided for gripping flexibility in the different labs. Given the

4” x 24” specimen, a 48” x 96” plate allowed removing 44 specimen: 11 from across the width

and 4 down the length.

2.4 Round Robin Rules

The rules of the round robin were explained in a document supplied to all labs when the

specimens were sent to the project participants. This document is the second document included

in Appendix A. The philosophy and rules for testing were described in detail. The intent of this

round robin was to evaluate the characteristics of the E647 test standard. As such, the

fundamental rules involved were few and simple:


• Rule #1. Any method permitted by ASTM E647 to generate FCG data is

allowed (i.e. the organizers are not confining testing to one test methodology, for instance).

• Rule #2. The supplied specimens must be tested to the conditions indicated. • Rule #3. The testing must be performed to the ΔK regime indicated for the

materials (see Table 2-5). • Rule #4. Results and procedures must be fully documented so that the data

can be analyzed and the results understood (this point was really critical in view of the large amount of data involved in this testing).

The intention in this round robin was not to dictate the test conditions but rather to allow the user

to choose, within the overall context of E647, the method suitable for the laboratory

instrumentation. Summarized in Table 2-5 are the conditions that were to be evaluated during

the round robin. Note that the last column in Table 2-5 identifies the specific condition denoted

in each shipment to the testing labs.

When a round robin test program is performed, the participants are typically instructed in

detail specifically what needs to be done. The problem with this approach with the E647 test

standard is the complexity of the test and the inability of different labs to have duplicate

capabilities. For instance, one lab may be used to performing FCG tests under constant

amplitude loading. It would not be fair to ask them to perform a test using K-control methods.

Conversely, one lab may be making visual crack length measurements whereas another lab may

rely on compliance to yield continuous crack length measurements. Restricting testing to only

one method was not deemed valuable since testing labs are using different approaches in

practice. Using this approach then suggests that the variability measured would correspond to

the variability apparent with the complete test standard.

Not all testing labs would be evaluating the same test conditions. However, as a general

rule the low load ratio fatigue crack growth behavior was the primary focus of the vast majority

of the testing. The envelope of growth rates indicated in Table 2-5 are provided for guidance

only in terms of selecting the conditions for testing. Achieving the full range of growth rate data

indicated by the upper and lower bound values in Table 2-5 is unlikely given the specimen sizes


involved in testing. Therefore, when test conditions were selected by a given laboratory, the

following were considered:

• For low load ratio testing (R = 0.1), the focus is on the growth rates starting at

ΔK = 10 ksi√in.

• For higher load ratio testing (R > 0.1), toughness limitations require generally starting lower, on the order of ΔK = 8 ksi√in. or so.

The participating laboratories were instructed to follow E647 whenever in doubt and attempting

to make decisions during testing.

With the relatively open philosophy (i.e. not confining testing to certain techniques and

methods) used during this testing, the burden when evaluating the results was to understand how

the testing was performed so as to potentially determine the roles of different variables. This

means that there is additional burden on the testing lab to provide sufficient, highly detailed

information to the round robin organizers concerning how testing was performed.

To assist in this process, Attachment A is provided at the end of the second document in

Appendix A to guide in the process of documenting the procedures followed. Items that are

included in this documentation include, for instance, complete detail regarding how the specimen

was precracked prior to starting the test. Once the test begins, details regarding how the

specimen was then loaded are also critically important. The analysis procedures used to create

the da/dN data are also important to document.

If non-visual crack length measurement was a part of a laboratory’s approach, it was

important to get a sense of the type of post-test correction strategy that was utilized to relate the

visual to the non-visual crack measurement data. Although a comparison between the visual

measurements and post-test corrected data would have been highly useful, none of the

participants utilizing this technique supplied this type of plot. Finally, it was important to ensure

that all of the elements that E647 requires in terms of reporting data were documented in the data

supplied to SwRI.


Table 2-1. Materials involved in the FCG round robin.

Product Size Alloy or Material Temper or Condition Thick or Cross Section Other Dimensions

4130 Normalized and heat treated 4” x ¼” bar 36 ft (cut in 18” segments) 7075 -T6 0.125” sheet 4’ x 8’ sheet 2024 -T351 0.375” sheet 4’ x 8’ sheet

Table 2-2. Tensile properties of the materials involved in the FCG round robin.

Material Specimen ID YS, ksi UTS, ksi elong, % RA, % 4130 steel RRL1 162.5 171.2 18.0 62.5

RRL2 158.5 167.3 17.0 62.7 RRL3 155.7 164.0 17.0 64.3 RRH1 171.3 181.0 17.0 63.8 RRH2 168.1 178.0 16.0 63.8 RRH3 169.4 179.8 17.0 65.4 RRD1 167.2 176.0 17.0 65.6 RRD2 169.1 178.2 17.0 62.6 RRD3 166.3 169.6 17.0 64.3 STRR-1 169.9 178.7 17.0 59.0 STRR-2 171.9 181.1 16.0 56.7 STRR-3 170.7 180.1 16.0 57.4 average 166.7 175.4 16.8 62.3 std. dev. 5.2 5.9 0.6 3.0

7075-T6 7xRR-1 77.3 83.8 14.3 29.3 7xRR-2 76.6 83.2 13.6 30.5 7xRR-3 75.0 83.0 14.3 31.0 average 76.3 83.3 14.1 30.3 std. dev. 1.2 0.4 0.4 0.9 HNDBK 72 80 n/a n/a

2024-T351 2xRR-1 55.7 71.6 22.0 31.6 2xRR-2 55.4 70.4 18.0 34.4 2xRR-3 56.3 72.5 20.0 33.7 average 55.8 71.5 20.0 33.2 std. dev. 0.5 1.1 2.0 1.5 HNDBK 50 66 n/a n/a

HNDBK = MMPDS, Reference [2], B-basis


Table 2-3. Round robin participants.

Test Laboratory Contact Person Country Data Complete

AFRL – Wright Labs Steve Thompson USA 1 / 06

Alcoa Rich Brazill USA 10 / 06

CNRCC Peter Au Canada 2 / 06

DSTO Kevin Walker Australia 9 / 07

Fatigue Technology Incorporated (FTI)

Joy Ransom USA 1 / 08

Fracture Technology Associates (FTA)

Keith Donald USA 11 / 07

Honeywell Jim Hartman USA 4 / 07

Martest Robert Diamond USA 5 / 07

Metcut Research Phil Bretz USA 4 / 05

NAL C. Manunatha India 8 / 05

NASA − Houston Royce Forman USA 8 / 06

NASA − Langley Scott Forth USA 9 / 05

NAVAIR – Pax River Mike Leap USA 3 / 06

Siemens Westinghouse Joe Anello USA 4 / 05

Southwest Research Institute (SwRI)

Jim Feiger USA 1 / 06

US Air Force Academy Scott Fawaz USA 11 / 06

US Naval Academy Rick Link USA 3 / 06

Westmoreland Jim Rossi USA 2 / 06


Table 2-4. Specimen IDs for different materials and specimen geometries.

Material Thickness

B, in. SpecimenGeometry Notched?

WidthW, in.

No. ofSpec.

Specimen Identification Nos.

4130 steel ∼0.25 C(T) yes 2.0 24 2xy where x = A-K, y = 1-4

C(T) yes 3.0 27 3xy where x = A-K, y = 1-4

2024-T351 ∼0.25 C(T) no 2.0 30 W2-2-x where x = 1-30

C(T) no 3.0 29 W3-2-x where x = 1-30

∼0.375 M(T) no 4.0 32 AL-2- x where x = 1-32

7075-T6 ∼0.125 M(T) no 4.0 44 AL-7- x where x = 1-44

Table 2-5. Generalized test conditions for the round robin testing including approximate lower

and upper bound envelope of crack growth rates and applied stress intensity factor ranges.

Lower Bound Upper Bound

Material Thickness

B, in. Load Ratio

ΔK, ksi√in.

da/dN, in./cyc

ΔK, ksi√in.

da/dN, in./cyc

Round-RobinCondition ID

4130 steel ∼0.25 0.1 8 1 (10-7) 30 6 (10-6) STL-A

0.8 8 3 (10-7) 30 1 (10-5) STL-B

2024-T351 ∼0.25 0.1 5 2 (10-7) 40 1 (10-3) ALX-A

0.5 5 1 (10-6) 20 1 (10-4) ALX-C

∼0.375 0.1 5 2 (10-7) 40 1 (10-3) ALX-B

7075-T6 ∼0.125 0.1 5 3 (10-7) 40 1 (10-3) ALN-A


Figure 2-1. Notch requirements for the different specimens in E647-05 [1]

(scanned from standard).


Figure 2-2. Specimen geometries extracted from E647-05 [1] illustrating specimen dimensions for both C(T) and M(T) configurations (scanned from standard).


3.0 ANALYSIS METHODOLOGY The primary result from a fatigue crack growth rate test is the crack growth rate (da/dN) as

a function of applied stress intensity factor range (ΔK). Comparing results from two crack

growth rate tests is complicated by not having similar ΔK levels to make a growth rate

comparison. To overcome this issue and compare on an equal ΔK basis, an analysis

methodology was developed as will be described in this section of the report. This approach is

extracted from work originally performed in support of ASTM activities [3].

3.1 Interpolating Crack Growth Rate Data

The absence of similar ΔK levels for comparative assessment can be overcome by applying

an interpolation scheme to a given data set. This approach will be further described with a

typical crack growth rate data set shown in Figure 3-1. Although this is a single data set, if we

were to repeat this test and plot the data, the basic problem is that none of the applied ΔK levels

would agree with each other. Consequently, the data set shown in Figure 3-1 needs to be re-

characterized in a standard manner to allow comparison to other data sets.

The basic approach consists of first dividing up each ΔK decade into ten equal increments

in log space. These increments are shown schematically with the data in Figure 3-2 (only the

increments relevant to the range of the data are shown). The approach now will be to interpolate

the data to determine the effective da/dN for the different increments. The interpolation scheme

is further described in Figure 3-3 focusing on the 100.7 (which equals 5.012) interval.

The points that are interpolated are the two points bridging the incremental ΔK level.

These adjacent points are connected by the straight line in Figure 3-3. In order to interpolate, the

functional relationship between the fatigue crack growth rate points must be known. The Paris

relationship, namely da/dN = C ΔKm, is used herein to provide this link. On a log-log plot such

as Figure 3-3, this relationship is linear with a slope of m and a da/dN intercept of magnitude

logC. The functional form is approximate; however, the closer the ΔK data is spaced, the less

difference functional form matters.


The actual interpolation for the data shown in Figure 3-3 is further described by the

calculations in Figure 3-4. The actual steps to calculate the interpolated da/dN point in

Figure 3-3 are in Figure 3-4. This algorithm was implemented in a generalized FORTRAN

program to perform this interpolation on a given set of da/dN versus ΔK data. This method was

used to characterize each set of da/dN data generated during the round robin test program.

3.2 Applying the Interpolation Approach

Prior to applying this automated approach to the round robin data, it was first applied to

data that came from ASTM committee work. Eric Tuegel leads a Structural Applications

(E08.04) Task Group E08.04.04 entitled, “Variability, Statistics and Probabilistic Modeling”.

This group was provided 74 sets of Alcoa lot release data from fatigue crack growth rate testing.

This data was supplied by Markus Heinimann and Rich Brazill (both from Alcoa) with ΔK

normalized by some toughness level to guard the proprietary nature of the data (it is unknown to

us what aluminum alloy is considered). For reference, the data are reproduced in Figure 3-5.

To provide context for the statistical analysis, the focus of this effort is to assess the

variability in the context of what E647 suggests. For reference, the portion of E647 [1] related to

precision and bias is reproduced below:

“…variability in da/dN versus ΔK is available from results of an interlaboratory test program in which 14 laboratories participated. These data, obtained on a highly homogeneous 10 Ni steel, showed the reproducibility in da/dN within a laboratory to average ±27% and range from ±13 to ±50%, depending on laboratory; the repeatability between laboratories was ±32%. Values cited are standard errors based on ±2 residual standard deviations about the mean response determined from regression analysis. In computing these statistics, abnormal results from two laboratories were not considered due to improper precracking and suspected errors in force calibration…..”

The original round robin study for E647 referenced in the preceding paragraph can be found

documented in Reference [4]. It should be kept in mind that ±32% difference implies an

absolute range of approximately 2x on FCG rate (±32% = 1.32/0.67 ∼ 2). Similarly, a difference

of ±50% would imply a factor of 3x and ±27%, a factor of 1.75x.


The data set interpolation was carried out with a simple FORTRAN program that parsed

the input data. Once interpolated, the individual da/dN’s at a given ΔK level were statistically

processed to determine averages and standard deviations. Note that averages and standard

deviations (see Table 3-1) are in log(da/dN) space. For reference, Figure 3-6 provides the

average da/dN values at nine levels (see Table 3-1 for the raw data). Note that the ninth level, at

ΔK normalized to 1, included only five data points (interpolated results from only 5 of the 74)

and hence was not statistically processed any further since it was of limited value.

For reference, error bars are shown on top of the averages in Figure 3-7. These error bars

provide a range of ±2 standard deviations on the mean FCG rate responses. The associated

variability range of these standard deviations numbers, computed in the last column of Table 3-1,

are shown in Figure 3-8 for the Alcoa74 data. In this plot, the variability is noted as a FCGR

ratio (as described earlier). Note that the average variability was 2.008 which is extremely

similar to the quoted ASTM level. However, the Alcoa74 data was supplied from one lab but it

represented seventy-four different lots of material, so any further comment relative to what is

stated in E647 is not possible. Moreover, for further reference, the distributions of the da/dN

data (interpolated, of course) are shown for each ΔK level in Figure 3-9. The distributions

appear fairly log-normal or normal in type.

Although the data included in this section of the report was ΔK normalized and the

pedigree of the material is not supplied, the data nevertheless presented a unique opportunity to

develop and apply the statistical analysis approach intended for the round robin data with the

large number of da/dN values available for data processing.


Table 3-1. Tabulated values of average normalized growth rate (with standard deviation)

interpolated at various normalized ΔK levels.

Interpolated log(da/dN in in./cycle) value Interpolated

log(ΔK norm) No. of Pts. Average, A Std. dev., b da/dN variability range 10(A+2b)/10(A-2b) or 104b

-0.1 74 -3.8144 0.0791 2.072

-0.2 74 -4.2557 0.0871 2.230

-0.3 74 -4.4565 0.0716 1.933

-0.4 74 -4.6209 0.0631 1.789

-0.5 74 -4.8344 0.0475 1.549

-0.6 74 -5.1179 0.0582 1.709

-0.7 74 -5.5266 0.0834 2.157

-0.8 73 -6.1444 0.1278 3.244

Note: Number of points is the same as number of interpolated data sets.


ΔK, ksi√in1 10

da/d

N,

inch

/cyc

le

10-8

10-7

10-6

Figure 3-1. Sample set of fatigue crack growth rate data.

ΔK, ksi√in1 10

da/d

N,

inch

/cyc

le

10-8

10-7

10-6100.5 100.7 100.9 101.1 101.3

Figure 3-2. Sample set of fatigue crack growth rate

data with a subdivided ΔK in equal log space increments (10 per decade).

c:\data\pcm\rr\fr-round robin.doc

18 ΔK, ksi√in1 10

da/d

N,

inch

/cyc

le

10-8

10-7

10-610

0.510

0.9 101.3

ΔK, ksi√in

4 5 6 7

da/d

N,

inch

/cyc

le

2x10-8

3x10-8

4x10-8

5x10-8

6x10-8

100.7 100.8

(4.81, 3.31e-8)

(5.33, 4.26e-8)

Interpolated(100.7 or 5.012, 3.66e-8)

Figure 3-3. Interpolation strategy employed on either side of a ΔK increment.


Figure 3-4. Interpolation calculations for the data shown previously.


Alcoa Supplied Lot Release FCGR Test Data

Normalized K0.1 1

Fatig

ue C

rack

Gro

wth

Rat

e, i

nch/

cycl

e

10-7

10-6

10-5

10-4

10-3

10-2

datasets 1 - 20datasets 21 - 40datasets 41 - 60datasets 61 - 74

Figure 3-5. All of the data sets plotted on a common set of axes.



Normalized K0.1 1

Fatig

ue C

rack

Gro

wth

Rat

e, i

nch/

cycl

e

10-7

10-6

10-5

10-4

10-3

10-2

datasets 1 - 20datasets 21 - 40datasets 41 - 60datasets 61 - 74average (interpolated)

Figure 3-6. All data sets with the interpolated average da/dN rate at each normalized ΔK level.



Normalized K0.1 1

Fatig

ue C

rack

Gro

wth

Rat

e, i

nch/

cycl

e

10-7

10-6

10-5

10-4

10-3

10-2

datasets 1 - 20datasets 21 - 40datasets 41 - 60datasets 61 - 74average (interpolated)±2 standard deviations

Figure 3-7. All data sets with the interpolated average and ±2 SD’s on FCGR da/dN.



Normalized K0.1 1

FCG

R R

atio

, da

/dN

+2st

ddev

/ da

/dN

-2st

ddev

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Figure 3-8. Typical error bound of the crack growth rate data at a given normalized ΔK. Dashed lines indicate average with ±2 standard deviations of the mean.


Raw Data

-6.6 -6.5 -6.4 -6.3 -6.2 -6.1 -6.0 -5.9 -5.8

Cou

nt

0

2

4

6

8

10

12

14

16

Raw Data

-5.9 -5.8 -5.7 -5.6 -5.5 -5.4 -5.3

Cou

nt

0

2

4

6

8

10

12

14

16

18

Raw Data

-5.5 -5.4 -5.3 -5.2 -5.1 -5.0 -4.9

Cou

nt

0

5

10

15

20

25

30

35

Raw Data

-5.00 -4.95 -4.90 -4.85 -4.80 -4.75 -4.70 -4.65

Cou

nt

0

2

4

6

8

10

12

14

16

Raw Data

-4.80 -4.75 -4.70 -4.65 -4.60 -4.55 -4.50 -4.45

Cou

nt

0

2

4

6

8

10

12

Raw Data

-4.7 -4.6 -4.5 -4.4 -4.3 -4.2

Cou

nt

0

5

10

15

20

25

30

ΔΚnorm=10^(-0.8) ΔΚnorm=10^(-0.7)

ΔΚnorm=10^(-0.6) ΔΚnorm=10^(-0.5)

ΔΚnorm=10^(-0.4) ΔΚnorm=10^(-0.3)

Figure 3-9. Distribution of interpolated growth rates at a given normalized ΔK.


Raw Data

-4.6 -4.5 -4.4 -4.3 -4.2 -4.1 -4.0

Cou

nt

0

2

4

6

8

10

12

14

16

18

20

Raw Data

-4.1 -4.0 -3.9 -3.8 -3.7 -3.6 -3.5

Cou

nt

0

5

10

15

20

25

ΔΚnorm=10^(-0.2) ΔΚnorm=10^(-0.1)

Figure 3-9. continued


This page intentionally left blank.


4.0 RESULTS The purpose of this section of the report is to describe the data sets generated during the

round robin testing on a lab-per-lab basis. The fatigue crack growth results will also be

presented and summarized for each material and condition.

4.1 Final Matrix of Testing and Conditions

The testing performed for the six different FCG test conditions is summarized in Table 4-1.

In each of the boxes in Table 4-1, the number of tests/specimens that each lab performed for each

different test condition is described. Five of the 18 labs performed all of the crack growth test

conditions. The condition evaluated by the greatest number of labs was the low load ratio, C(T)

specimens fabricated from 2024 (ALX-A). The least common test condition, with eight labs,

was the high load ratio steel (STL-B). In total, 141 tests were performed during the round robin

testing. All but 37 of these (104 total), or 74%, were under low load ratio conditions. Most (2/3)

of the test specimens were compact tension specimens with the remainder being of the M(T)

geometry.

A comprehensive listing of the specimen ID Nos. tested in each lab is further shown in

Tables 4-2, 4-3 and 4-4. Each individual table treats two distinct material/test conditions.

However note that only the prefix of the specimen ID is included in this tabulated summary.

Any anomalies or unusual circumstances noted during testing are also indicated in the farthest

right column of the tabular summaries.

A summary is further provided in Table 4-5 that assesses some of the variables specifically

involved in the test procedures. Although most users supplied more detail, this particular

summary highlights some of the key variables including software utilized, crack length

measuring method and test mode (for instance, K-control or constant amplitude).


4.2 The Big Six

Plots of the fatigue crack growth rate curves for each of the six material/test conditions

comprise what is termed the “big six” plots in the context of this report. First, considering the

individual labs only, plots of all of the raw fatigue crack growth rate data are contained in

Appendix B. For each lab, the relevant plots of the six test conditions are presented in the

appendix, illustrating data from each of the individual tests. For reference, data from two labs

are shown in Figure 4-1 for the low R, M(T) specimen testing in condition ALX-B. In one case

(the plot inset on the left of Figure 4-1), three replicate data sets are included. This is contrasted

with the four data sets from the two specimens tested to similar conditions in Lab I in the plot

inset to the right in Figure 4-1. Note that both K-decreasing and K-increasing data are shown in

this plot as well as others in Appendix B.

Where possible, the data has been examined for E647 “validity”. It was not uncommon

that only da/dN-ΔK data was available, in which case the data was accepted as valid.

Occasionally, a spurious data point that was clearly an artifact was digitally eliminated (however

this was quite rare). When possible, the plasticity assessments in E647 were calculated and

compared to the test data to ensure that ligament yield conditions were not exceeded.

The big six plots are shown in Figures 4-2 through 4-7. In each of these plots, for a given

material/test condition, all of the data from each of the individual labs that tested this condition

are plotted coincidently on the same graph. For instance, Figure 4-2 depicts low R behavior of

4130 steel. Some fanning of the data is evident at lower ΔK as the crack growth response was

initiated.

The overall shape of the crack growth curves for the steel test conditions (Figures 4-2 and

4-3) appear fairly linear. Higher levels of curvature are clearly evident throughout the crack

growth rate curves for the 2024 and 7075 data shown in Figures 4-4, 4-5, 4-6 and 4-7. Low

apparent variability was evident for the low load ratio, 2024 condition tested with an M(T)

specimen (condition ALX-B shown in Figure 4-6). This condition is nearly identical to the C(T)

specimen (condition ALX-A shown in Figure 4-4) except the C(T) specimen was only 0.25-inch

thick as opposed to the 0.375-inch thick M(T). In and of itself, this thickness difference is not an


issue that we would expect to make a significant difference in fatigue crack growth rate behavior.

Either 0.25- or 0.375-inch would nominally be considered fairly thick-ish in section.


Table 4-1. Summary of round robin tests for each lab.

Number of Specimens for Each Test Condition ID Lab

ID No. STL-A STL-B ALX-A ALX-C ALX-B ALN-A

A 1 3 3

B 2 2 2 2 2 2

C 2 2 2

D 3 3

E 2 2 2 2

F 4 2 2 2 2 2

G 3 2 3 2 1 2

H 2 2 2

I 2 2 2 2 2 2

J 2 2 3

K 2 2 2

L 2 2 3 3

M 3 3

N 2 2

O 2 2 2

P 2 2 2 2 2 2

Q 2

R 1 3

Sum 28 17 29 20 23 24


Table 4-2. Specimen data summary for the 4130 steel material.

Specimen ID Nos.

Material B,

inch Load Ratio

ConditionID No.

Lab ID No. #1 #2 #3 #4

Miscellaneous Comments

4130 0.25 0.1 STL-A A 2F3 B 3C4 3E1 Missing 3E2, 3E3 data C 2K1 2K2 D no testing E 3L1 3L2 F 2A1 2B1 3A1 3A2 G 3B3 3B4 3C1 H 2J1 2J2 I 2G1 2G2 J 2I1 2I2 K 3F2 3G1 L no testing M no testing N 3K1 3K2 O 2J4 2J3 P 2C1 2E1 Q no testing R

4130 0.25 0.8 STL-B A 2F4 3B1 3B2 2F4, tested at wrong R B 3E4 3F1 C no testing D no testing E no testing F 3A3 3A4 G 3C2 3C3 H no testing I 2G3 2G4 J 2I3 2I4 K no testing L no testing M no testing N 3I1 3I2 O no testing P 2F1 2F2 Q no testing R


Table 4-3. Specimen data summary for the thinner 2024-T351 aluminum material.

Specimen ID Nos.

Material B,

inch Load Ratio

ConditionID No.

Lab ID No. prefix #1 #2 #3


2024 0.25 0.1 ALX-A A W2-2 -5 -6 -7 B W3-2 -10 -11 missing -12, -13 data C W2-2 -23 -24 D no testing E W2-2 -29 -30 F W3-2 -1 -2 G W3-2 -5 -6 -7 H W2-2 -15 -16 I W2-2 -8 -9 J W2-2 -12 -13 -14 K W3-2 -16B -28B L W3-2 -18 -19 M no testing N no testing O W2-2 -19 -20 P W2-2 -1 -2 Q no testing R

2024 0.25 0.5 ALX-C A no testing B W3-2 -14 -15 C W3-2 -22 -23 D no testing E no testing F W3-2 -3 -4 G W3-2 -8 -9 H W2-2 -17 -18 I W2-2 -10 -11 J no testing K W3-2 -17B -29B tested at R = 0.7 L W3-2 -20 -21 M no testing N no testing O W2-2 -21 -22 P W2-2 -3 -4 Q no testing R


Table 4-4. Specimen data summary for thicker 2024-T351 and 7075-T6 aluminum materials.

Specimen ID Nos.

Material B,

inch Load Ratio

ConditionID No.

Lab ID No. prefix #1 #2 #3


2024 0.375 0.1 ALX-B A no testing B AL-2 -10 -11 C no testing D AL-2 -26 -27 -28 E AL-2 -20 -21 F AL-2 -1 -2 G AL-2 -7 -8 -9 -7/-8 invalid data, side-

to-side crk. len. diffs. H no testing I AL-2 -5 -6 Side-to-Side

diff > 0.025W J no testing K no testing L AL-2 -12 -13 -14 M AL-2 -29 -30 -33 -28 dup (-33) N no testing O no testing P AL-2 -3 -4 Q none 001 301 Re-machined C(T)’s R AL-2 25

7075 0.125 0.1 ALN-A A no testing B AL-7 -9 -10 C no testing D AL-7 -21 -22 -23 E AL-7 -30 -31 F AL-7 -1 -2 G AL-7 -7 -8 H no testing I AL-7 -5 -6 -5 overloaded

(censored) J no testing K no testing L AL-7 -11 -12 -13 M AL-7 -32 -33 -34 N no testing O no testing P AL-7 -3 -4 Q no testing R AL-7 -18 -19 -20


34

Table 4-5. Primary test conditions for the different labs involved in the round robin. Lab ID Control Software

Crack Length Measurement Humidity

Test Mode

Freq, Hz

da/dN calculation

A Digital FTA DCPD 45 K-inc. +3 20-30 secant B Analog FTA DCPD 20-30 CA: STL, ALX-C

K-inc.: ALX-A, ALX-B, ALN-A

10 secant

C Digital Instron compliance n/r K-dec. -2, CA 15 7 pt. poly D Digital in-house visual 40 CA 5-10 7 pt. poly E Analog FTA C(T): compliance

M(T): DCPD 40 CA: STL, ALX-B, ALN-A

K-inc. +2: ALX-A 20 mod. secant

F Analog in-house compliance 35-55 CA 20-30 mod. secant G Digital in-house DCPD 35-50 CA 10 secant, mod., 7 pt H Digital FTA DCPD 3-21 K-dec. -2, CA 20 mod. secant I Analog FTA C(T): compliance

M(T): DCPD 21-57 K-dec. -2, K-inc. +2 10 7 pt. poly

J Analog in-house compliance n/r K-inc. +2, +2.5 25-35 secant K Digital FTA KRAK-gage 40 K-inc. +1.7, +2.25, +3 50 mod. secant L Digital FTA compliance 30-50 K-inc. +1.8, +2.25 25 7 pt. poly M Digital in-house/MTS visual: ALX-B

compliance: ALN-A 15-25 CA: ALX-B

K-inc. +2.5: ALN-A 15 secant

N Digital FTA DCPD 15-30 K-dec. -2, K-inc +5 1-5 7 pt. poly, mod. sec. O n/r n/r DCPD 45 K-inc. +1.6 20 7 pt. poly P Digital MTS compliance 25-60 CA 10 secant Q Analog FTA compliance 40 K-dec. -5, K-inc. +2.5, +5 5-16 7 pt. poly R Digital MTS KRAK-gage 40 CA 10 secant


35

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

ΔK, MPa√m10 100

da/d

N, m

/cyc

le

10-9

10-8

10-7

10-6

10-5

10-4

Lab D (AL-2-27)Lab D (AL-2-28)Lab D (AL-2-26)

Material:B:

Geometry:R:

2024-T3510.375-inchM(T)0.1

ΔK, MPa√m10 100

da/d

N, m

/cyc

le

10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab I (AL-2-5 K-increase)Lab I (AL-2-6 K-increase)Lab I (AL-2-5 K-decrease)Lab I (AL-2-6 K-decrease)

Material:B:

Geometry:R:

2024-T3510.375-inchM(T)0.1

Figure 4-1. Typical data sets from two of the participating laboratories (all data is plotted in Appendix B).


ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab A (1 Set)Lab B (2 Sets)Lab C (2 Sets)Lab E (2 Sets)Lab F (4 Sets)Lab G (3 Sets)Lab H (2 Sets)Lab I (2 Sets)Lab J (2 Sets)Lab K (2 Sets)Lab N (2 Sets)Lab O (2 Sets)Lab P (2 Sets)

Material:B:

Geometry:R:

4130 Steel0.25-inchC(T)0.1

Figure 4-2. All fatigue crack growth associated with test condition STL-A.


ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

Lab A (3 Sets)Lab B (2 Sets)Lab F (2 Sets)Lab G (2 Sets)Lab I (2 Sets)Lab J (2 Sets)Lab N (2 Sets)Lab P (2 sets)

Material:B:

Geometry:R:


Figure 4-3. All fatigue crack growth associated with test condition STL-B.


ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab A (3 Sets)Lab B (2 Sets)Lab C (2 Sets)Lab E (2 Sets)Lab F (2 Sets)Lab G (3 Sets)Lab H (2 Sets)Lab I (2 Sets)Lab J (3 Sets)Lab K (1 Set)Lab L (2 Sets)Lab O (2 Sets)Lab P (2 Sets)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.1

Figure 4-4. All fatigue crack growth associated with test condition ALX-A.


ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab B (2 Sets)Lab C (2 Sets)Lab F (2 Sets)Lab G (2 Sets)Lab H (2 Sets)Lab I (2 Sets)Lab L (2 Sets)Lab O (2 Sets)Lab P (2 Sets)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.5

Figure 4-5. All fatigue crack growth associated with test condition ALX-C. Note that the unusual features of the lab 0 data will be discussed elsewhere in this report.


ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab B (2 Sets)Lab D (3 Sets)Lab E (2 Sets)Lab F (2 Sets)Lab G (1 Set)Lab I (2 Sets)Lab L (3 Sets)Lab M (3 Sets)Lab P (2 Sets)Lab Q (2 Sets)Lab R (1 Set)

Material:B:

Geometry:R:

2024-T3510.375-inchM(T)0.1

Figure 4-6. All fatigue crack growth associated with test condition ALX-B.


ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab B (2 Sets)Lab D (3 Sets)Lab E (2 Sets)Lab F (2 Sets)Lab G (2 Sets)Lab I (2 Sets)Lab L (3 Sets)Lab M (3 Sets)Lab P (2 Sets)Lab R (3 Sets)

Material:B:

Geometry:R:

7075-T60.125-inchM(T)0.1

Figure 4-7. All fatigue crack growth associated with test condition ALN-A.


5.0 DISCUSSION Whereas the previous section of the report presented the basic results, these results are

further examined in this section, and the statistics associated with variability are calculated and

described. The intent of this discussion is a thorough, though not exhaustive, analysis of the

round robin results; this analysis provides a framework that others can work with in the future to

determine the full implications of these results.

The key quantity from all of the statistical analysis is a measure of data variability.

Essentially variability is represented as the range of da/dN growth rate at a given ΔK level. It

can be calculated from the standard deviation (b) of the average log(da/dN) data. In terms of a

factor on growth rate, variability can be quantified by the ratio 10(a+2b)/10(a-2b) which can be

algebraically shown to be 104b (recall that the average log(da/dN) is the quantity “a” and the

standard deviation is “b”). Two variability measures are possible: one representing variability

across all of the labs (interlaboratory variability) or one representing variability within a lab

(intralaboratory variability). In this section of the report, both of these variability measures will

be quantified and further examined.

5.1 Analysis Methodology

The basic tool utilized in analyzing these results was described in Chapter 3 of this report.

A FORTRAN program was utilized to provide interpolated fatigue crack growth rate data

specific ΔK levels. Each decade of ΔK was divided up into ten equal log(ΔK) intervals and each

of these levels was used to calculate an interpolated da/dN growth rate. This method was applied

to each individual crack growth data set to yield an array of different growth rate points

(essentially key da/dN at fixed ΔK points). These data were then processed as indicated in

Figure 5-1 importing the interpolated data into a spreadsheet for further statistical analysis.

The analysis method is schematically shown in Figure 5-1. At the heart of the approach is

the interpolated da/dN data at different vertical lines of the lab data, with each different column

representing sequential ΔK magnitudes. Averages and standard deviations summed down each

column represent interlaboratory analysis. This is contrasted to the summation for each lab to


the right side of the basic data. Whereas Figure 5-1 illustrates the approach with a schematic,

Figure 5-2 provides an actual portion of the spreadsheet with the three analysis zones clearly

evident for one of the six material test conditions (ALN-1 which is thin 7075 material tested at a

low R-ratio condition using M(T) specimens).

5.2 Interpolated Data for Each of the Six Conditions

The interpolated results for each of the six round robin test conditions are shown in

Tables 5-1 through 5-3 and in Figures 5-3 through 5-8. In each of these figures, two plots are

shown: an FCG rate plot (left side) and a variability plot (right side). The FCG rate plot on the

left side in Figure 5-3 illustrates all of the round robin experimental data (the gray data points)

with the distinct interpolated data overlaid with the average (open data point) bounded by error

bars that represent a ±2 standard deviations growth rate range. The growth rate variability range,

expressed as a ratio, is depicted in the plot shown on the right side of Figure 5-3. This plot

indicates how variability changes with applied ΔK level.

A close examination of the six plots in Figures 5-3 through 5-8 shows that in general the

range represented by ±2 standard deviations on the average growth rate provides an excellent

description of the observed range of the growth rate data. There are clear instances where data is

beyond this range, but in the vast majority the range does an excellent job capturing the extent of

the data. There is no clear trend of variability with applied ΔK level (the plots on the right-hand

side of Figures 5-3 to 5-8). In some cases, for instance STL-A in Figure 5-3, the variability is

constant with a jump at the highest ΔK as the data fans out in the Stage III regime. This is

contrasted to STL-B in Figure 5-4 where the highest variability is observed at the start due to

fanning at the lowest ΔK level.

Some variability differences are apparent in what one would nominally consider to be

similar test conditions. For instance, if the 2024,low R, C(T) data (Figure 5-5) is compared with

that from the M(T) specimens (identical test conditions) in Figure 5-7, the overall level of scatter

is much greater for the C(T) when contrasted to the M(T) specimen. Is this effect real or simply

a consequence one condition including more data than the other (i.e. comparing results from few

data to results from many data and seeing more effect from the tail of the growth rate


distributions)? To answer this question, the number of tests involved in each condition is

described in Table 4-1. An examination of the data suggests:

• condition ALX-A – 29 data sets from 13 labs (Figure 5-5, C(T) specimen

geometry), and • condition ALX-B – 23 data sets from 11 labs (Figure 5-7, M(T) specimen

geometry).

Hence the more scattered data in Figure 5-5 is from 25% more labs than the less scattered data in

Figure 5-7. However, with both populations totaling over 20 data sets, it is unlikely that the

difference is due to sampling effects since each includes a high number of tests.

Variability can also be a function of the “shape” of the fatigue crack growth rate curve. In

the case of the two steel material test conditions (Figures 5-3 and 5-4), the growth rate curve is

fairly linear and the observed variability is for the most part constant as a function of ΔK. This is

contrasted to varying levels of variability for 2024 that is partially due to the shape of the growth

rate curve. This is most clearly understood by examining the data in Figure 5-7. As the lowest

ΔK level, the near vertical growth rate curve and fanning of the data causes a higher level of

variability. At the fourth interpolated data point (ΔK about 6 ksi√in), the variability also

increases as a consequence of the knee in the da/dN-ΔK data. As the growth rate data becomes

more vertical, the associated level of scatter will increase. Finally, as the Stage III regime is

approached, an increase in variability is apparent at the third to last interpolated point (the

decrease in variability at the higher ΔK points is simply due to fewer data sets, see detail in

Table 5-3).

The influence of number of data sets is probably most striking in Figure 5-8 where the

7075 was fatigue tested. For the first two and last two interpolated points, where variability

levels are quite low, there were less than 10 data sets (on average) included in the calculation.

This is contrasted to the middle region of the growth rate data where greater than 20 data sets

were involved.


5.3 Interlaboratory Variability

There are numerous ways to calculate overall variability levels. In Figures 5-3 to 5-8, the

da/dN variability ratio is plotted for each interpolation point. However one usually isn’t

interested in variability as a function of ΔK; rather, an overall variability that applies to a given

material test condition would likely be desired. One way to quantify the overall variability for a

given material condition is to simply average the da/dN variability ratios (resulting in the average

da/dN variability ratio). Hence, the ratio is averaged and in effect the averaging occurs “outside”

of the log space. This is contrasted to the idea of averaging the standard deviations “b” for each

ΔK level, resulting in an average b level and simply calculating 104b where bavg is used for the

calculation. In a sense, this averaging is occurring “inside” of the log space. In theory, both of

these parameters should be the same as long as both are normal distributions.

Both of these approaches for determining an overall variability level are used in Figure 5-9

for the six material test conditions. The plot shown in Figure 5-9(a) averages the variability ratio

whereas in Figure 5-9(b) the average b is calculated and then used in 104b to calculate an average

variability. A close examination of Figure 5-9 shows that little difference is apparent between

the two calculation methods. Averaging the ratio yields slightly higher average variability

levels. The difference ranges from 3% higher for the steel to 12% higher for the 2024 C(T)

specimens. On average the average of the variability ratio is 7% higher than the variability

derived from an average b.

It is interesting to compare the different conditions in Figure 5-9(b) to examine the possible

impact on variability. First, with regard to the 4130 steel data, the observed variability is the

lowest for this material considering both high and low load ratio (approximately a factor of 1.9x

on FCG rate on average). However for the 2024 aluminum, compact tension specimens yielded

a variability of approximately 2.5x compared to 1.8x for the M(T) specimens. Note that these

specimens were fabricated from the same material but the M(T) specimens retained full

thickness (0.375-inch) and the C(T) was machined thinner (0.25 inch). It is unlikely that much

of the variability difference was due to the slight thickness difference although the thicker M(T)

specimens would retain surface microstructure that could differ from processing. Ignoring

microstructural differences (if they exist), the foregoing conclusion is that specimen geometry


influences variability, with M(T) specimens exhibiting 30-40% less variability when compared

to C(T) specimens. If this difference is true, it suggests that 7075 material variability is greater

than 2024 since the variability for ALN-A (2.56) with the M(T) geometry is on par with the C(T)

2024 variability (2.63).

5.4 Intralaboratory Variability

The same two measures are used in Figure 5-10 to compare intralaboratory variability.

Note that the basic data for these plots is included in Tables 5-4 to 5-6. Several observations are

notable. First, the difference between the two methods of quantifying variability is similar to

that observed previously. The variability level when b is averaged (as opposed to averaging the

variability ratio) is slightly less with the minor variability difference caused from analysis

methodology. The observed intralaboratory variability level is less than the interlaboratory

variability magnitude. Furthermore, less difference is noted between conditions when

intralaboratory variability is used as the measure. In other words, regardless of material,

specimen geometry or load ratio, da/dN error is typically on the order of 1.50-1.75x.

Similar trends in variability are observed when either the inter- and intralaboratory

measures are compared. Given this, the key question then is whether there is a functional

relationship between the two. To understand this, the two measures are plotted against each

other for both the average da/dN variability ratio and the average variability in Figure 5-11. This

plot suggests that there appears to be a linear link between the two, with the intralaboratory

variability trending toward 30-40% of the interlaboratory variability. However the observation

of nonzero intralaboratory variability when interlaboratory variability is zero makes little

physical sense. Although the correlation coefficient is high, its value and the observation of a

nonzero intercept suggests that the link between inter- and intralaboratory variability is weak at

best.

5.5 Variability Summary

For the purposes of this summary, the da/dN variability measure quoted is the average

da/dN variability determined from 104b where b is the calculated average value. Based on


Figures 5-9 and 5-10 and the data tabulated in Tables 5-1 to 5-6, the following observations can

be made:

• The variability in crack growth rate measurement observed with steel is slightly less than with aluminum. Considering like specimen geometries, thicker 2024 appears to have less variability than thin 7075. The M(T) specimen geometry exhibits 30-40% less variability when compared to the C(T) specimen geometry.

• The interlaboratory variability for steel is approximately 1.9x whereas for

aluminum it is 2.4x. For 2024 and 7075 the mean growth rate variabilities are 2.3x and 2.6x, respectively.

• The intralaboratory variability for steel is approximately 1.5x whereas for

aluminum it is 1.65x. For 2024 and 7075 the mean growth rate variabilities are 1.6x and 1.7x, respectively.

• The intralaboratory variability observed basically ranged from a low of 1.2x to

typically 2.5-3.0x for all of the materials combined. The two steel conditions had intralaboratory variability levels that ranged from 1.2x to 2.4x.

Given these observations, the natural question is: How do they compare to the earlier

round robin assessment of E647 performed 33 years ago [4]? The paragraph from E647 in

Section 3.2 of this report suggests that the previous interlaboratory variability was quantified as

1.94x (1.32/0.68). This is contrasted to previous intralaboratory variability that was on average

1.74x (1.27/0.73) with a range of 1.3x (1.13/0.87) to 3.0x (1.5/0.5). The previous round robin

examined only one material, a highly homogeneous rotor steel.

In the strictest sense, only the steel results can be compared to the previous round robin

study results. With:

• overall interlaboratory variability of 1.9x (now) versus 1.94x (then), and

• overall intralaboratory variability of 1.5x (now) versus 1.74x (then) with a range for steel of 1.2x to 2.4x (now) versus 1.3x to 3.0x (then),

it can be concluded that there is little statistical difference between variability levels today versus

33 years ago. These data suggest that it can be argued that the intralaboratory variability has

likely decreased some. However, on balance when everything is averaged between labs, crack


growth rate measurement variability is approximately the same between labs as it was 33 years

ago. Although these results are somewhat disappointing, they likely suggest that the process

simply has an inherent variability level that improvements in technology can not overcome.

Finally, it should also be pointed out that previous to the testing in this round robin, no replicate

data existed regarding variability for aluminum material. 5.6 Individual Laboratory Performance The intralaboratory variabilities presented in Tables 5-3 to 5-6 are replotted in Figures 5-12

to 5-14 illustrating individual lab performance. In these bar charts, the blue bars are for labs that

exhibited better than average variability whereas the yellow bars are for labs that exceeded the

average variability level. It is interesting to note that the statistics tend to be dominated by the

occasional lab that had high variability. This is reflected in the fewer number of yellow bars

when compared to the blue bars in Figures 5-12 to 5-14.

It should be noted that in generating these statistics, no attempt has been made to censor the

data, with one notable exception. One lab, Lab O, generated data for condition ALX-C

(Figure 5-13(b)) where the variability was 14.6x. This level is clearly way out of bounds with

the other data and was censored in any data processing examining overall variability. This issue

will be explored in additional detail in a subsequent section of this report.

Individual lab performance is further assessed by examining how an individual lab’s

variability compared to the mean level in Figure 5-15. For each of the six material conditions,

the position relative to the mean level was ranked as shown in the balloons in the schematic at

the top of Figure 5-15. Each variability level position was then recorded and if it happened to be

in the top 3 or top 2 lowest variabilities (depending on number of labs), this was also noted. The

matrix of positions is shown in the matrix in the lower part of Figure 5-15. The matrix in

Figure 5-15 provides individual labs with a generic, qualitative metric that they can use to assess

performance. The laboratories with minimum variability in Figure 5-15 appear to be Lab E (all

four conditions in lowest variability quartile), Lab B (half in lowest quartile, remainder better

than average), Lab G (half in lowest quartile, remaining half better than average) and Lab P

(40% in lowest quartile, remaining portion better than average).


5.7 Statistical Anomalies?

There is danger when averaging is used to represent overall behavior. As an example of

this, examine the performance of Lab Q for condition ALX-B in Figure 5-14(a). The average

da/dN variability ratio is 1.44x when all the data is included. However, a close examination of

the data for Lab Q, shown in Figure 5-16, indicates that the average value is skewed by two

outlier variability levels in the near threshold (the initial, lowest ΔK interpolation point) and the

knee in the data (the fourth lowest ΔK interpolation point). If these points are omitted from the

averaging, as they should be since they simply represent erroneous values due to the shape of the

growth rate curve, the average variability drops from 1.44x to 1.15x. A close examination of

Figure 5-14(a) shows that 1.15x would be the lowest value observed of all the laboratories. So,

the implication is that if all the data is treated as a whole, results can be skewed by features that

do not necessarily reflect the nature of the growth rate data measured.

The opposite is true for the condition that exhibited the highest variability in the whole

round robin. Recall that Lab O exhibited a variability of 14.6x as observed in Figure 5-13(b) for

the high load ratio, 2024 test condition. The two data sets that this variability was derived from

are shown in Figure 5-17(a). Clearly, the data sets are disparate and inconsistent, as sometimes

occurs in practice. A close examination of the data suggests that there is no apparent reason for

the difference and there does not appear to be anything in the processed data that would suggest

why a large difference would be manifested. Therefore, in this case the observed variability does

represent reality.

The two foregoing examples do not represent the typical data observed for most of the

laboratories. A more typical result that represents the majority of observed behavior is shown in

Figure 5-18 where the resulting statistics make sense relative to the overall data. A close

examination between the data and the interpolation points clearly illustrates that the error bar

range encompasses the experimental data. Therefore the average da/dN variability ratio, in this

case 1.47x, is consistent with the variability apparent in the actual crack growth rate data.


5.8 Influence of Test Technique Variables

The purpose of constructing Table 4-5 was to determine the range of test techniques

utilized by the different laboratories. Although in some cases it was difficult to extract the

information from the documentation supplied by each lab to construct Table 4-5, it is believed

that the data shown captures the major experimental differences between the approaches used in

the different laboratories. The obvious question is whether different methods result in different

levels of variability. Although this question can likely be answered rigorously using an

advanced statistical assessment, an engineering approach is presented herein to isolate a couple

of the key variables and assess whether there are clear differences in crack growth variability.

Two characteristics of test technique will be examined. First, the method used for crack length

measurement will be examined followed by the load control mode used during the test.

Four methods were used for crack length measurement at all of the laboratories: DCPD,

compliance, KRAK gages (indirect DCPD) and near-continuous visual measurements. The only

methods that had sufficient numbers of labs (3 or greater) to examine possible differences were

DCPD and compliance. The average “b” parameter (standard deviation on crack growth rate) is

indicated in Table 5-7 as a function of material condition, laboratory and crack length

measurement method. As can be observed, the number of laboratories involved is likely

sufficient to produce statistically meaningful data.

The overall difference in variability is summarized in Figure 5-19(a). It is interesting to

note that for the three material conditions examined, there is a systematic difference in behavior

with variability levels less for DCPD when compared to compliance. However, in a strict

numerical sense the statistician would argue that the standard deviations captured in Table 5-7

are too high to come to a strict statistical conclusion. Nevertheless, from an engineering

viewpoint there does appear to be some difference due to the two measurement techniques.

Keep in mind that other variables were perturbed in this cross section: most notably material

(steel and aluminum) and specimen geometry (all C(T) except for ALN-A which is an M(T)

specimen geometry). It is surprising that the least difference is noted for condition ALN-A

where DCPD is easiest and compliance is most difficult (due to insensitivity related to little

stiffness change).


It is tempting to suggest that DCPD yields less variability than compliance. However,

Lab E had lower variability using compliance for two of three cases compared to all DCPD

participants. Hence, it is not easy to decouple overall trends. Finally, whereas DCPD may on

average yield lower variability, is it actually easier or harder to do well?

A similar comparison is provided for constant amplitude loaded conditions versus K-

control methods in Table 5-8 and Figure 5-19(b). Whereas some difference was apparent for

crack length measurement method, there does not appear to be any real difference between test

load control method. All of these techniques appear to yield approximately the same variability

level.


Table 5-1. Processed statistics for the different labs testing condition STL-A and STL-B.

Material Condition

log(ΔK) magnitude

Average Parameter, A

Std. Dev. Parameter, b

Number of Points

Variability104b

STL-A 0.7 -7.385 n/a 1 n/a 0.8 -7.081 0.0583 3 1.710 0.9 -6.761 0.0549 13 1.659 1 -6.448 0.0796 23 2.082 1.1 -6.165 0.0842 26 2.172 1.2 -5.894 0.0931 29 2.358 1.3 -5.647 0.0766 30 2.024 1.4 -5.387 0.0641 28 1.805 1.5 -5.136 0.0423 25 1.476 1.6 -4.935 0.0440 18 1.499 1.7 -4.696 0.0587 14 1.717 1.8 -4.411 0.0758 11 2.011 1.9 -4.078 0.0661 7 1.839 2 -3.745 0.1349 3 3.465 2.1 -3.203 n/a 1 n/a

STL-B 0.6 -7.182 0.1197 6 3.012 0.7 -6.950 0.0464 6 1.534 0.8 -6.721 0.0925 9 2.343 0.9 -6.553 0.0884 11 2.257 1 -6.310 0.0765 14 2.023 1.1 -6.054 0.0662 14 1.840 1.2 -5.791 0.0494 14 1.577 1.3 -5.510 0.0441 12 1.501 1.4 -5.218 0.0549 9 1.657 1.5 -4.902 0.0594 7 1.728


Table 5-2. Processed statistics for the different labs testing condition ALX-A and ALX-C.

Material Condition

log(ΔK) magnitude



Number of Points

Variability104b

ALX-A 0.5 -7.213 0.0399 2 1.444 0.6 -6.941 0.0336 4 1.363 0.7 -6.776 0.0682 15 1.874 0.8 -6.543 0.1260 20 3.192 0.9 -5.681 0.1603 28 4.378 1 -5.234 0.0935 28 2.365 1.1 -4.935 0.0527 26 1.624 1.2 -4.660 0.0952 25 2.404 1.3 -4.387 0.1170 26 2.936 1.4 -3.973 0.1600 22 4.363 1.5 -3.475 0.1080 13 2.705 1.6 -2.841 0.2057 4 6.649 1.7 -2.131 n/a 1 n/a

ALX-C 0.3 -8.190 0.0719 2 1.938 0.4 -7.074 0.0224 2 1.230 0.5 -6.911 0.0093 2 1.090 0.6 -6.650 0.0895 8 2.280 0.7 -6.215 0.2000 14 6.311 0.8 -5.695 0.0896 22 2.282 0.9 -5.321 0.0933 24 2.362 1 -5.009 0.1169 20 2.935 1.1 -4.712 0.1154 17 2.894 1.2 -4.398 0.1202 11 3.026 1.3 -3.904 0.1341 9 3.440


Table 5-3. Processed statistics for the different labs testing condition ALX-B and ALN-A.

Material Condition

log(ΔK) magnitude



Number of Points

Variability 104b

ALX-B 0.5 -7.403 0.1528 2 4.087 0.6 -6.862 0.0758 7 2.009 0.7 -6.684 0.0389 9 1.431 0.8 -6.343 0.1103 17 2.761 0.9 -5.488 0.0531 18 1.631 1 -5.129 0.0281 21 1.295 1.1 -4.833 0.0373 21 1.410 1.2 -4.547 0.0814 18 2.117 1.3 -4.239 0.0598 15 1.734 1.4 -3.834 0.1016 12 2.550 1.5 -3.294 0.0455 8 1.520 1.6 -2.739 0.0101 2 1.098

ALN-A 0.6 -6.534 0.0278 5 1.292 0.7 -6.034 0.0833 8 2.154 0.8 -5.408 0.1306 16 3.330 0.9 -5.083 0.1179 20 2.962 1 -4.820 0.1626 21 4.471 1.1 -4.572 0.1289 22 3.279 1.2 -4.388 0.1411 18 3.667 1.3 -4.084 0.0971 16 2.445 1.4 -3.678 0.0552 10 1.663 1.5 -3.112 0.0756 7 2.006


Table 5-4. Laboratory-by-laboratory breakout for testing conditions STL-A and STL-B.

Statistics for b parameter Material

Condition Lab ID average 2 std. dev. min max

Variability104b

STL-A Lab I 0.0164 0.0164 0.0045 0.0257 1.163

Lab E 0.0235 0.0417 0.0012 0.0655 1.242

Lab G 0.0238 0.0299 0.0103 0.0558 1.245

Lab H 0.0280 0.0333 0.0085 0.0575 1.294

Lab O 0.0293 0.0439 0.0031 0.0652 1.310

Lab P 0.0332 0.0679 0.0024 0.0774 1.357

Lab B 0.0335 0.1063 0.0066 0.1533 1.362

Lab N 0.0353 0.0155 0.0246 0.0431 1.385

Lab J 0.0551 0.1030 0.0195 0.1142 1.661

Lab F 0.0636 0.0506 0.0134 0.0899 1.796

Lab K 0.0926 0.1180 0.0257 0.1802 2.347

Lab C 0.0956 0.0716 0.0190 0.1373 2.411

STL-B Lab I 0.0215 0.0118 0.0128 0.0262 1.219

Lab B 0.0289 0.0331 0.0034 0.0538 1.306

Lab P 0.0349 0.0186 0.0242 0.0458 1.379

Lab G 0.0369 0.0432 0.0099 0.0665 1.405

Lab N 0.0458 0.0717 0.0034 0.0910 1.524

Lab A 0.0547 0.0601 0.0276 0.1143 1.655

Lab F 0.0694 0.0814 0.0395 0.1399 1.896


Table 5-5. Laboratory-by-laboratory breakout for testing conditions ALX-A and ALX-C.



Variability104b

ALX-A Lab E 0.0182 0.0343 0.0018 0.0566 1.182

Lab B 0.0215 0.0276 0.0018 0.0447 1.219

Lab G 0.0308 0.0303 0.0113 0.0506 1.328

Lab F 0.0563 0.1680 0.0070 0.2057 1.680

Lab H 0.0584 0.0739 0.0227 0.1212 1.712

Lab L 0.0689 0.1373 0.0048 0.1933 1.887

Lab A 0.0749 0.0480 0.0472 0.1180 1.994

Lab C 0.0796 0.1581 0.0153 0.2180 2.081

Lab J 0.0876 0.1346 0.0307 0.2285 2.240

Lab I 0.0989 0.0776 0.0618 0.1719 2.486

ALX-C Lab B 0.0157 0.0238 0.0009 0.0323 1.155

Lab G 0.0218 0.0517 0.0002 0.0699 1.223

Lab P 0.0301 0.0309 0.0192 0.0410 1.320

Lab L 0.0301 0.0347 0.0078 0.0537 1.320

Lab H 0.0425 0.0536 0.0013 0.0760 1.479

Lab I 0.0455 0.0422 0.0247 0.0733 1.521

Lab K 0.0476 0.0682 0.0085 0.0921 1.551

Lab F 0.0842 0.2380 0.0210 0.2626 2.171

Lab C 0.1094 0.1294 0.0320 0.2066 2.740

Lab O 0.2909 0.0391 0.2672 0.3070 14.577


Table 5-6. Laboratory-by-laboratory breakout for testing conditions ALX-B and ALN-A.



Variability104b

ALX-B Lab M 0.0118 0.0174 0.0009 0.0247 1.115

Lab D 0.0272 0.0405 0.0106 0.0562 1.285

Lab E 0.0316 0.0875 0.0012 0.1180 1.337

Lab B 0.0316 0.0689 0.0011 0.0987 1.337

Lab Q 0.0398 0.0905 0.0030 0.1528 1.443

Lab P 0.0402 0.0138 0.0316 0.0479 1.448

Lab F 0.0720 0.0601 0.0380 0.0950 1.941

Lab I 0.0839 0.1823 0.0317 0.2204 2.165

ALN-A Lab I 0.0106 0.0175 0.0042 0.0206 1.102

Lab E 0.0159 0.0195 0.0031 0.0264 1.158

Lab P 0.0409 0.0875 0.0080 0.1284 1.458

Lab L 0.0420 0.0504 0.0054 0.0814 1.472

Lab B 0.0497 0.0612 0.0049 0.0938 1.580

Lab D 0.0504 0.0551 0.0217 0.0766 1.591

Lab G 0.0583 0.0824 0.0070 0.1008 1.711

Lab M 0.0708 0.0714 0.0301 0.1167 1.919

Lab R 0.1291 0.1594 0.0694 0.2421 3.284


Table 5-7. Influence of crack length measurement technique (DCPD

and compliance) on overall variability levels.

Average b parameter Measurement Method

Lab ID STL-A ALX-A ALN-A

DCPD A 0.074911

B 0.033516 0.021489 0.049695

E 0.015925

G 0.023788 0.03081 0.058282

H 0.027970 0.058393

I 0.010553

N 0.035349

O 0.029323

Avg. = 0.029989 0.046401 0.033614

2σ = 0.009173 0.049265 0.047775

COMPL. C 0.095567 0.079563

E 0.023517 0.018186 0.015925

F 0.063594 0.056311

I 0.016431 0.098868 0.010553

J 0.055101 0.087573

L 0.068938 0.042007

M 0.070785

P 0.033172 0.040904

Avg. = 0.047897 0.068240 0.036035

2σ = 0.059141 0.057189 0.048173


Table 5-8. Influence of loading method (constant amplitude versus

K-control) on overall variability levels.

Average b parameter Load Type

Lab ID STL-A ALX-A ALN-A

CA B 0.033516 C 0.095567 0.079563 D 0.050442 E 0.023517 0.015925 F 0.063594 0.056311 G 0.023788 0.030810 0.058282 H 0.027970 0.058393 P 0.033172 0.040904 R 0.129085 Avg. = 0.043018 0.056269 0.058928 2σ = 0.053843 0.039921 0.084667

K-control A 0.074911 B 0.021489 0.049695 E 0.018186 I 0.016431 0.098868 0.010553 J 0.055101 0.087573 K 0.092626 L 0.068938 0.042007 M 0.070785 N 0.035349 O 0.029323 Avg. = 0.045766 0.052388 0.052010 2σ = 0.059350 0.065110 0.063242


61

FCG Round Robin Data Analysis Approach

ALN-AB(AL-7-9)4.525094 5.25398E-074.563668 5.57183E-074.616022 6.02534E-074.656641 5.94642E-074.71172 6.76569E-074.75313 7.77531E-074.806246 7.76214E-07etc etc etc.....

ΔK and da/dNraw data

FORTRANprogram

ProjectID=ALN-A FileID=B(AL-7-9) 0.70 -6.015229 0.80 -5.399114 0.90 -5.1489631.00 -4.933981 1.10 -4.763005 1.20 -4.512465 1.30 -4.166721 1.40 -3.692932 1.50 -3.164770 FileID=B(AL-7-10)0.70 -6.087634 0.80 -5.524089 etc....etc....etc....

log(ΔK) intervals and interpolated da/dN (each file)

labs

log(ΔK)

data fromFORTRANprogram

InterlaboratoryStatistics

labs

log(ΔK)

IntralaboratoryStatistics

spreadsheet

Figure 5-1. Schematic approach of how spreadsheet-based analysis is organized.


62

Log DK levels across the top, log dadN down below Log DK levels across the top, log dadN down below

0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5

FileID=B(AL-7-9) -6.01523 -5.39911 -5.14896 -4.93398 -4.76301 -4.51247 -4.16672 -3.69293 -3.16477 Lab B AVG A = -6.05143 -5.4616 -5.21528 -4.9805 -4.76646 -4.52906 -4.14592 -3.67364 -3.12019FileID=B(AL-7-10) -6.08763 -5.52409 -5.2816 -5.02703 -4.76992 -4.54566 -4.12513 -3.65434 -3.07561 STD b = 0.051198 0.088371 0.093791 0.065794 0.004888 0.023474 0.029411 0.027287 0.063046

numpts = 2 2 2 2 2 2 2 2 2N ratio = 1.602479 2.256746 2.372262 1.83306 1.046044 1.241353 1.31113 1.285719 1.787239

FileID=D(AL-7-21) -4.60048 -4.45104 -4.12819 Lab D AVG A = -4.92411 -4.65466 -4.46418 -4.16571 -3.73534 -3.18314FileID=D(AL-7-22) -4.48918 -4.20322 -3.73534 -3.18314 STD b = 0.076619 0.021657 0.05305FileID=D(AL-7-23) -4.92411 -4.70884 -4.45233 numpts = 1 2 3 2 1 1

N ratio = 2.025243 1.220753 1.630046

FileID=E(AL-7-30) -4.70712 -4.4424 -4.17169 -3.95959 -3.66526 Lab E AVG A = -4.99548 -4.7 -4.44456 -4.18889 -3.94096 -3.65408 -3.18637FileID=E(AL-7-31) -4.99548 -4.69288 -4.44672 -4.20609 -3.92233 -3.64289 -3.18637 STD b = 0.010066 0.003061 0.024327 0.026352 0.015822

numpts = 1 2 2 2 2 2 1N ratio = 1.097149 1.028595 1.251141 1.274697 1.156874

FileID=F(AL-7-1-d) -6.48492 AVG A = -6.52587 -6.14871 -5.57208 -5.33189 -5.23854FileID=F(AL-7-2-d) -6.54697 Lab F STD b = 0.035468FileID=F(AL-7-2-i) -6.54571 -6.14871 -5.57208 -5.33189 -5.23854 numpts = 3 1 1 1 1

N ratio = 1.386346

FileID=G(AL-7-7) -5.36404 -5.02297 -4.69407 -4.41496 -4.18393 -4.02133 -3.67627 Lab G AVG A = -5.35912 -5.0392 -4.74533 -4.4862 -4.24634 -4.02133 -3.67627FileID=G(AL-7-8) -5.35421 -5.05542 -4.79659 -4.55744 -4.30875 STD b = 0.006954 0.022948 0.072493 0.100754 0.088262

numpts = 2 2 2 2 2 1 1N ratio = 1.066148 1.235361 1.949724 2.52938 2.254484

FileID=I(AL-7-5-d) -4.46997 Lab I AVG A = -5.00365 -4.71423 -4.44625 -4.17498 -3.91265FileID=I(AL-7-5-i) -5.00422 STD b = 0.006867 0.004226 0.020567FileID=I(AL-7-6-d) -5.01022 -4.71125 -4.43341 numpts = 3 2 3 1 1FileID=I(AL-7-6-i) -4.99652 -4.71722 -4.43536 -4.17498 -3.91265 N ratio = 1.065294 1.039687 1.208559

FileID=L(AL-7-11) -5.38723 -5.09382 -4.90746 -4.70721 -4.53997 -4.16159 -3.63263 -3.12193 Lab L AVG A = -6.54731 -5.92178 -5.32875 -5.03278 -4.81911 -4.63557 -4.5125 -4.1693 -3.63514 -3.09885FileID=L(AL-7-12) -6.54348 -5.88441 -5.27192 -4.99885 -4.80263 -4.63548 -4.52022 -4.18597 -3.65534 -3.07576 STD b = 0.005406 0.052849 0.057674 0.05297 0.081373 0.071595 0.032037 0.014454 0.019067 0.03265FileID=L(AL-7-13) -6.55113 -5.95915 -5.32709 -5.00567 -4.74723 -4.56402 -4.47731 -4.16033 -3.61746 numpts = 2 2 3 3 3 3 3 3 3 2

N ratio = 1.05105 1.627034 1.700973 1.628843 2.115877 1.93366 1.343218 1.142395 1.191981 1.350827

FileID=M(AL-7-32) -6.04438 -5.65117 -5.21247 -4.95271 -4.76611 -4.47631 -4.07779 Lab M AVG A = -6.05858 -5.58987 -5.16693 -4.88885 -4.6895 -4.4747 -4.08351 -3.80344 -2.97732FileID=M(AL-7-33) -6.03313 -5.66314 -5.23659 -4.9248 -4.70983 -4.50396 -4.04937 -3.80344 -2.97732 STD b = 0.034808 0.116698 0.100488 0.087546 0.088541 0.030093 0.037323FileID=M(AL-7-34) -6.09825 -5.4553 -5.05174 -4.78906 -4.59257 -4.44384 -4.12336 numpts = 3 3 3 3 3 3 3 1 1

N ratio = 1.377946 2.929498 2.523195 2.239677 2.260285 1.319383 1.410233

FileID=P(AL-7-3) -5.29421 -4.9312 -4.63559 -4.38192 -4.27767 -3.98255 Lab P AVG A = -5.28855 -4.94589 -4.65613 -4.40586 -4.25975 -4.07336FileID=P(AL-7-4) -5.2829 -4.96058 -4.67667 -4.4298 -4.24184 -4.16417 STD b = 0.007995 0.020775 0.029044 0.033854 0.025332 0.128424

numpts = 2 2 2 2 2 2N ratio = 1.076411 1.210875 1.306705 1.365893 1.262782 3.263594

FileID=R(AL-7-18) -5.30165 -5.19893 -4.56292 -4.50621 Lab R AVG A = -5.32555 -5.11 -4.78076 -4.58645FileID=R(AL-7-19) -5.27126 -4.96361 -4.73791 -4.65989 STD b = 0.069399 0.127749 0.242128 0.077062FileID=R(AL-7-20) -5.40374 -5.16747 -5.04145 -4.59326 numpts = 3 3 3 3

N ratio = 1.894943 3.243367 9.300629 2.033519

AVG A = -6.53444 -6.03386 -5.4077 -5.08341 -4.82006 -4.57222 -4.38762 -4.08402 -3.67759 -3.11213STD b = 0.027824 0.083324 0.130612 0.117901 0.162601 0.128946 0.141074 0.097075 0.055215 0.075556

numpts = 5 8 16 20 21 22 18 16 10 7N ratio = 1.292104 2.154249 3.330032 2.962127 4.471011 3.279334 3.666868 2.445129 1.662871 2.005508

DK = 3.981072 5.011872 6.309573 7.943282 10 12.58925 15.84893 19.95262 25.11886 31.62278dadN = 2.92E-07 9.25E-07 3.91E-06 8.25E-06 1.51E-05 2.68E-05 4.1E-05 8.24E-05 0.00021 0.000772

10^(A+2b) = 3.32E-07 1.36E-06 7.14E-06 1.42E-05 3.2E-05 4.85E-05 7.84E-05 0.000129 0.000271 0.00109410^(A-2b) = 2.57E-07 6.3E-07 2.14E-06 4.79E-06 7.16E-06 1.48E-05 2.14E-05 5.27E-05 0.000163 0.000545

Interlaboratory Variability

Avg N 2stdev N min max2.726923 1.974085 1.292104 4.471011

Avg b 2stdev b min max 10^4b0.102013 0.083693 0.027824 0.162601 2.55889

Figure 5-2. Actual portion of the spreadsheet analysis for condition ALN-1.


63

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

Material:B:

Geometry:R:

4130 steel0.25-inchC(T)0.1

ΔK, ksi√in

1 10 100

da/d

N v

aria

bilit

y ra

tio, 1

0(A+2

b)/1

0(A-2

b)

0

1

2

3

4

5

6Material:

B:Geometry:

R:


(a) (b)

Figure 5-3. Comparing the interpolated data with the actual for condition STL-A in terms of (a) growth rate and (b) variability.


64

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

Material:B:

Geometry:R:


ΔK, ksi√in

1 10 100

da/d

N v

aria

bilit

y ra

tio, 1

0(A+2

b)/1

0(A-2

b)

0

1

2

3

4

5

6Material:

B:Geometry:

R:


(a) (b)

Figure 5-4. Comparing the interpolated data with the actual for condition STL-B in terms of (a) growth rate and (b) variability.


65

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Material:B:

Geometry:R:

2024 aluminum0.25-inchC(T)0.1

ΔK, ksi√in

1 10 100

da/d

N v

aria

bilit

y ra

tio, 1

0(A+2

b)/1

0(A-2

b)

0

1

2

3

4

5

6Material:

B:Geometry:

R:


(a) (b)

Figure 5-5. Comparing the interpolated data with the actual for condition ALX-A in terms of (a) growth rate and (b) variability.


66

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Material:B:

Geometry:R:


ΔK, ksi√in

1 10 100

da/d

N v

aria

bilit

y ra

tio, 1

0(A+2

b)/1

0(A-2

b)

0

1

2

3

4

5

6Material:

B:Geometry:

R:


(a) (b)

Figure 5-6. Comparing the interpolated data with the actual for condition ALX-C in terms of (a) growth rate and (b) variability.


67

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Material:B:

Geometry:R:

2024 aluminum0.375-inchM(T)0.1

ΔK, ksi√in

1 10 100

da/d

N v

aria

bilit

y ra

tio, 1

0(A+2

b)/1

0(A-2

b)

0

1

2

3

4

5

6Material:

B:Geometry:

R:


(a) (b)

Figure 5-7. Comparing the interpolated data with the actual for condition ALX-B in terms of (a) growth rate and (b) variability.


68

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Material:B:

Geometry:R:


ΔK, ksi√in

1 10 100

da/d

N v

aria

bilit

y ra

tio, 1

0(A+2

b)/1

0(A-2

b)

0

1

2

3

4

5

6Material:

B:Geometry:

R:


(a) (b)

Figure 5-8. Comparing the interpolated data with the actual for condition ALN-A in terms of (a) growth rate and (b) variability.


STL-A STL-B ALX-A ALX-C ALX-B ALN-A

aver

age

da/d

N v

aria

bilit

y ra

tio, 1

0(A+2

b)/1

0(A-2

b)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4130 steel2024 aluminum7075 aluminum

Low R, C(T)

High R, C(T)

Low R, C(T)

High R, C(T)

Low R, M(T)

Low R, M(T)

1.99 1.95

2.94

2.71

1.97

2.73

Interlaboratory

(a)


aver

age

da/d

N v

aria

bilit

y, 1

04b

0

1

2

3


Low R, C(T)

High R, C(T)

Low R, C(T)

High R, C(T)

Low R, M(T)

Low R, M(T)

1.94 1.90

2.632.43

1.84

2.56

Interlaboratory

(b)

Figure 5-9. Average interlaboratory variability using (a) range extent ratio and (b) exponent.



aver

age

da/d

N v

aria

bilit

y ra

tio, 1

0(A+2

b)/1

0(A-2

b)

0

1

2

3


Low R, C(T)

High R, C(T)

Low R, C(T)

High R, C(T)

Low R, M(T)

Low R, M(T)

1.621.52

2.031.85

1.61

1.83

Intralaboratory

(a)

STL-A STL-B ALX-A ALX-C ALX-B ALN-A0

1

2

3


Low R, C(T)

High R, C(T)

Low R, C(T)

High R, C(T)

Low R, M(T)

Low R, M(T)

1.55 1.48

1.781.61

1.511.70

Intralaboratory

aver

age

da/d

N v

aria

bilit

y, 1

04b

(b)

Figure 5-10. Average intralaboratory variability using (a) range extent ratio and (b) exponent.


Interlaboratory variability ratio1.75 2.00 2.25 2.50 2.75 3.00

Intr

alab

orat

ory

varia

bilit

y ra

tio

1.50

1.75

2.00


Vintra = 0.781 + 0.404 Vinter

r2 = 0.94

(a)

Interlaboratory variability1.75 2.00 2.25 2.50 2.75

Intr

alab

orat

ory

varia

bilit

y

1.4

1.5

1.6

1.7

1.84130 steel2024 aluminum7075 aluminum

Vintra = 0.942 + 0.299 Vinter

r2 = 0.88

(b)

Figure 5-11. Relationship between inter- and intralaboratory variability using

(a) range extent ratio and (b) exponent.


Lab

I

Lab

E

Lab

G

Lab

H

Lab

O

Lab

P

Lab

B

Lab

N

Lab

J

Lab

F

Lab

K

Lab

C

aver

age

da/d

N v

aria

bilit

y, 1

04b

0.0

0.5

1.0

1.5

2.0

2.5

3.0

average = 1.55

Material:B:

Geom:R:


(a)

Lab

I

Lab

B

Lab

P

Lab

G

Lab

N

Lab

A

Lab

F

aver

age

da/d

N v

aria

bilit

y, 1

04b

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

average = 1.48

Material:B:

Geom:R:


(b)

Figure 5-12. Intralaboratory variability for conditions (a) STL-A and (b) STL-B.


Lab

E

Lab

B

Lab

G

Lab

F

Lab

H

Lab

L

Lab

A

Lab

C

Lab

J

Lab

I

aver

age

da/d

N v

aria

bilit

y, 1

04b

0

1

2

3

average = 1.78

Material:B:

Geom:R:


(a)

Lab

B

Lab

G

Lab

P

Lab

L

Lab

H

Lab

I

Lab

K

Lab

F

Lab

C

Lab

O

aver

age

da/d

N v

aria

bilit

y, 1

04b

0.0

0.5

1.0

1.5

2.0

2.5

3.0

14.015.0

average = 1.61

Material:B:

Geom:R:


(b)

Figure 5-13. Intralaboratory variability for conditions (a) ALX-A and (b) ALX-C.


Lab

M

Lab

D

Lab

E

Lab

B

Lab

Q

Lab

P

Lab

F

Lab

I

aver

age

da/d

N v

aria

bilit

y, 1

04b

0

1

2

3

average = 1.51

Material:B:

Geom:R:


(a)

Lab

I

Lab

E

Lab

P

Lab

L

Lab

B

Lab

D

Lab

G

Lab

M

Lab

R

aver

age

da/d

N v

aria

bilit

y, 1

04b

0

1

2

3

average = 1.70

Material:B:

Geom:R:


(b)

Figure 5-14. Intralaboratory variability for conditions (a) ALX-B and (b) ALN-A.


Lab

M

Lab

D

Lab

E

Lab

B

Lab

Q

Lab

P

Lab

F

Lab

I

aver

age

da/d

N v

aria

bilit

y, 1

04b

0

1

2

3

average = 1.51

Material:B:

Geom:R:


-1-2

+1+2+3

+4+5+6

TOP 3

better than average

Lab Performance and ranking for each of the different specimen/test conditions ID STL-A STL-B ALX-A ALX-C ALX-B ALN-A A -2 -2 B +2 +3 (top 2) +4 (top 2) +7 (top 3) +3 +2 C -4 -3 -2 D +5 (top 3) +1 E +7 (top 3) +5 (top 2) +4 (top 3) +5 (top 3) F -2 -3 +2 -1 -1 G +6 (top 3) +1 +3 +6 (top 3) -1 H +5 +1 +3 I +8 (top 3) +4 (top 2) -5 +2 -2 +6 (top 3) J -1 -4 K -3 +1 L -1 +4 +3 M +6 (top 3) -2 N +1 -1 O +4 -3 P +3 +2 +5 (top 3) +1 +4 (top 3) Q +2 R -3

Figure 5-15. Laboratory performance for the different conditions (plot shows ranking method).


76

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Material:B:

Geometry:R:


Lab Q Data

ΔK, ksi√in

1 10 100

da/d

N v

aria

bilit

y ra

tio, 1

0(A+2

b)/1

0(A-2

b)

0

1

2

3

4

5

6 Material:B:

Geometry:R:

2024 aluminum0.375-inchC(T)0.1 Lab Q Data

avg 1.15(ΔK > 8)

avg 1.44(full range ΔK)

initialthreshold

kneein data

(a) (b)

Figure 5-16. Comparing the interpolated data with the actual for Lab Q data in terms of (a) growth rate and (b) variability.


77

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Material:B:

Geometry:R:


Lab O Data

ΔK, ksi√in1 10 100

da/d

N v

aria

bilit

y ra

tio, 1

0(A+2

b)/1

0(A-2

b)

0

5

10

15

20Material:

B:Geometry:

R:


Lab O Data


(a) (b)

Figure 5-17. Anomalous Lab O data in terms of (a) growth rate (compared to interpolated) and (b) variability.


78

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Material:B:

Geometry:R:


Lab L Data

ΔK, ksi√in

1 10 100

da/d

N v

aria

bilit

y ra

tio, 1

0(A+2

b)/1

0(A-2

b)

0

1

2

3

4

5

6Material:

B:Geometry:

R:


Lab L Data


(a) (b)

Figure 5-18. More typical Lab L data in terms of (a) growth rate (compared to interpolated) and (b) variability.


STL-A ALX-A ALN-A

aver

age

da/d

N v

aria

bilit

y, 1

04b

0.0

0.5

1.0

1.5

2.0

DCPD compliance

1.22x1.18x1.02x

(a)

STL-A ALX-A ALN-A

aver

age

da/d

N v

aria

bilit

y, 1

04b

0.0

0.5

1.0

1.5

2.0

constant amplitudeK-control

1.03x0.96x 0.94x

(b)

Figure 5-19. Effect of (a) crack length measurement method and (b) test control variability.


6.0 SUMMARIZING AND CONCLUDING REMARKS The results of an extensive round robin are presented in this report that involved testing at

18 laboratories. Three materials were utilized during this round robin: high strength 4130 steel,

2024-T351 plate in two thicknesses (0.25 inch and 0.375 inch) and 7075-T651 sheet 0.125-inch

thick. Six test conditions were evaluated during the round robin. Three test conditions involved

the 2024 aluminum, two examined the 4130 steel and one concerned the 7075 aluminum. In

total, 141 fatigue crack growth tests were performed in accordance with ASTM E647 during this

round robin. Seventy-four percent of these tests were under low load ratio conditions and nearly

2/3 of the test samples were of the compact tension specimen geometry.

Although the testing involved in this program was extensive, there are a number of

summarizing observations that emerge from this work. These include:

• Of the large number of tests performed, only two tests from one material

condition were censored from the overall statistical analysis due to excessive

variability1. An examination of the limited data available suggests that there

is no discernable technical reason to omit this data, but it was sufficiently out-

of-bounds that it was censored. The other data supplied by this laboratory was

consistent with results generated by other laboratories. No other data was

eliminated from the analysis.

• One of the natural problems with fatigue crack growth rate data is that from

data set to data set there are no exactly matchable points. For this reason, a

technique was used to represent each data set in a standard manner. This

standard manner, codified in a FORTRAN program, consisted of interpolating

between da/dN-ΔK data points to calculate growth rates at distinct ΔK levels.

These distinct ΔK levels correspond to each decade of ΔK divided up into ten

equal (log space) intervals. The da/dN-ΔK data supplied by the laboratories

1 Lab O, 2024 data, Figure 5-17.


was sufficiently fine and continuous in nature that the interpolation applied

resulted in no discernable additional error.

• An assessment of variability in growth rate was made at different ΔK levels.

In this context, variability refers to the scatter in fatigue crack growth rate at a

given ΔK level. Average variability refers to an average magnitude over the

complete range of ΔK’s evaluated. In this context variability is represented as

the ratio of maximum to minimum da/dN rate where the range is represented

by ±2 standard deviations on log(da/dN). Comparisons were made between

variability measures by averaging growth rate ratios (outside of log space) or

simply the average standard deviation (inside log space). Little engineering

difference in variability was noted.

• The steel material exhibited the lowest level of interlaboratory variability. For

steel the growth rate variability observed is 1.9x whereas for aluminum it is

2.4x. For 2024 and 7075, the mean interlaboratory growth rate variabilities

are 2.3x and 2.6x, respectively.

• The intralaboratory variability for steel is 1.5x whereas for aluminum it is

1.65x. For 2024 and 7075, the mean intralaboratory growth rate variabilities

are 1.6x and 1.7x, respectively. The intralaboratory variability ranged from a

low of 1.2x to typically 2.5-3.0x for all of the materials combined. The two

steel conditions had intralaboratory variability levels that ranged from 1.2x to

2.4x.

• It is useful to compare current variability levels to those obtained in 1975 [4].

Recall that the previous study utilized only a rotor steel, so therefore results

are only made to the two steel conditions evaluated herein. In summary:

overall interlaboratory variability of 1.9x (circa 2008) versus 1.94x (1975),

and, overall intralaboratory variability of 1.5x (2008) versus 1.74x (1975)

with a range for steel of 1.2x to 2.4x (2008) versus 1.3x to 3.0x (1975).

Therefore there is little statistical difference between variability levels in 2008


versus 1975. The data suggests a slight decrease in intralaboratory variability

may have occurred, although from a rigorous statistical viewpoint this finding

may be arguable.

• The round robin data reported herein suggest that specimen geometry can

impact variability. For instance, using data from C(T) and M(T) specimens at

low load ratio in 2024 material, the M(T) specimens exhibited variability

levels that were 30-40% less than similar C(T) specimens.

• A comparison between tests performed using DCPD and compliance as the

continuous, non-visual crack length measurement method suggests that

variability levels are 20% less for DCPD when compared to compliance.

Differences were observed for all of the three material/test conditions

examined. Conversely, no discernable difference in variability level was

noted between different load control methods (constant amplitude versus K-

control techniques).


7.0 REFERENCES [1] “E647-05: Standard Test Method for Measurement of Fatigue Crack Growth Rates,”

Annual Book of ASTM Standards, Section 3, Volume 3.01, ASTM International, West Conshohocken, PA, 2007.

[2] Metallic Materials Properties Development and Standardization (MMPDS), DOT/FAA/

AR-MMPDS-01, U.S. Department of Transportation, Federal Aviation Administration, Office of Aviation Research, January 2003.

[3] Email and personal correspondence, Eric J. Tuegel, AFRL, on behalf of ASTM E08.04. [4] Clark, Jr., W. G. and Hudak, Jr., S. J., “Variability in Fatigue Crack Growth Rate Testing,”

Journal of Testing and Evaluation, JTEVA, Vol. 3, No. 6, 1975, pp. 454-476.

ASTM International takes no position respecting the validity of any patent rights asserted in connection with any item mentioned in this research report. Users of this research report are expressly advised that determination of the validity of any such patent rights, and the risk of infringement of such rights, are entirely their own responsibility.

This research report is copyrighted by ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States. Individual reprints (single or multiple copies) of this research report may be obtained by contacting ASTM at the above address or at 610-832-9585 (phone), 610-832-9555 (fax), or serviceastm.org (e-mail); or through the ASTM website (www.astm.org).

Appendix A – Supporting Guiding (Pretest) Documentation

S O U T H W E S T R E S E A R C H I N S T I T U T E ®

6220 CULEBRA RD. 78238-5166 • P.O. DRAWER 28510 78228-0510 • SAN ANTONIO, TEXAS, USA • (210) 684-5111 • WWW.SWRI.ORG

HOUSTON, TEXAS (713) 977-1377 • WASHINGTON, DC (301) 881-0226

Mechanical & Materials Engineering Division October 29, 2003 Recipient (see list at end of letter) Re: ASTM Round Robin on Fatigue Crack Growth Rate Measurement (ASTM E647) Dear xxx, Nearly thirty years ago, shortly after the initial draft of ASTM E647, a round robin was held to determine the precision and bias of the test method. Since this time, the standard has considerably evolved along with the hardware and control systems that we use to perform the tests. For approximately the past five years, a new round robin to assess where we currently stand has been (gradually) developing. The focus of this new round robin is to measure the fatigue crack growth rate behavior, as per ASTM E647, in the Paris regime using any of the possible perturbations allowed by the test standard. The desired range of ΔK (and a specific R-ratio) will be stated along with other details regarding the testing procedures when specimens are delivered. The intent is to not be overly restrictive; we want to insure that the standard is exercised in as broad a manner as possible simply insuring that we know in detail the procedures utilized (so as to subsequently statistically analyze the data). Three materials were procured for this work including:

• 0.125-inch thick 7075-T6 sheet, • 0.375-inch thick 2024-T351 plate, and, • 0.250-inch thick 4130 steel tempered to 175 ksi UTS.

These three materials were selected to represent a range of crack growth behavior. Since purchasing, the materials have been machined into a variety of specimens (180 total) as indicated in Table 1.

Table 1. Listing of the specimens available for fatigue crack growth rate round robin testing.

Material Specimen No. of W, B, Notch Specification Geometry1 Specimen inch inch Length2

4130 steel C(T) 24 2 0.25 0.2 C(T) 26 3 0.25 0.2

2024-T351 C(T) 30 2 0.25 none 29 3 0.25 none M(T) 28 4 0.375 0.2

7075-T6 M(T) 43 4 0.125 0.2 In planning the round robin, it is anticipated that different labs will have available different levels of resources for this effort. We have attempted to categorize the level of effort and number of variables that could be captured. These different levels of involvement are broadly indicated below along with a brief description of their scope:

1 M(T) specimens are all 23-inch long with no additional modifications made to the grip region. 2 Represented by either a/W (compact tension) or 2a/W (middle cracked tension).




• Full Participation – all three materials, two r-ratios, approximately 15-18 tests • Partial Level A – all three materials, low r-ratio only, 9-12 tests • Partial Level B – two materials only, mostly low r-ratio (exclusively, depending upon number of

specimens possible), 6-9 tests Please note that these are recommendations only and are based on an attempt to rationalize how the results will be analyzed once testing is completed. I think you will agree that we have an unprecedented opportunity with the broad range of materials and specimens available to capture the role of variability in the measurement of fatigue crack growth rate. It is anticipated that this exercise could provide guidance for where the ASTM standard needs additional definition, clarification or further focus. The purpose of this transmission is to solicit your support and the support of your organization for this round robin. Clearly the success of the round robin is dependent upon insuring that we have a sufficient number of participants. Therefore, my questions to you are the following:

• Would you be willing to participate in this round robin by providing the required testing and subsequent analysis of your data?

• If so, what specimen geometries and types can be integrated into your facility? What level of

participation can you provide? How many specimen can you test and how many would you like delivered?

• If not, do you have a recommendation of a laboratory that might participate in this effort (note the

addresses of the recipients of this letter provided on the following page)?

• Alternatively, is there another possibly more relevant point of contact in your organization that you could forward this transmission to?

I would appreciate a response to these questions by 5 December 2003 so that we can continue to make progress on this effort. Recognizing that we are all donating our time and energy to this effort gratis, I am hesitant to impose time constraints on when the testing needs to be completed. Practically, our goal is to complete testing in calendar year 2004.

Please do not hesitate to contact me if you have any further questions. Myself and the technical community within ASTM certainly appreciate your time and effort. We feel this is an important and worthwhile effort to insure the health of the ASTM fatigue crack growth standard. I look forward to hearing from you soon. Sincerely,

ljm Peter C. McKeighan Manager – Mechanical Testing Section voice: 210.522.3617 • fax: 210.522.6965 e-mail: [email protected]



List of Recipients John J. Ruschau University of Dayton Research Institute 300 College Park Dayton, OH 45469-0136 Steven R. Thompson Air Force Research Laboratory AFRL/MLSC Building 652, Room 122 2179 12th St. Wright-Patterson AFB, OH 45433-7718 Mike Leap Naval Air Warfare Center – Aircraft Div. Metals, Ceramics and NDE Branch Code 4.3.4.2, M/S 5 Building 2188 48066 Shaw Rd. Patuxent River, MD 20670 Joy Ransom Fatigue Technology Inc. 401 Andover Park East Seattle, WA 98188-7605 Keith Donald Fracture Technology Associates 2412 Emrick Blvd. Bethlehem, PA 18020 Prof. Ralph Stephens The University of Iowa Department of Mechanical Engineering Iowa City, IA 52242 Prof. Bob Stephens University of Idaho Mechanical Engineering Department Moscow, ID 83843 Prof. Dale Wilson Tennessee Tech University Department of Mechanical Engineering 115 W. 10th St. Box 5034

Cookeville, TN 38505-0001 Scott Forth NASA Langley Research Center Mechanics and Durability Branch MS 188E 2 West Reid St. Hampton, VA 23681 Prof. Sheldon Mostovoy ITT Department of MMAE 10 West 32nd St. Chicago, IL 60616 Carl Rousseau Bell Helicopter TEXTRON Post Office Box 482 Fort Worth, TX 76101 Prof. Rick Neu Georgia Institute of Technology School of Mechanical Engineering Atlanta, GA 30332-0245 Prof. Ashok Saxena Georgia Institute of Technology School of Materials Science and Eng. Atlanta, GA 30332-0245 Jim Rossi Westmoreland Mechanical Testing Old Rt. 30, Westmoreland Dr. P.O. Box 388 Youngstown, PA 15696-0388 Bob Somerville Boeing Commercial Airplane Group P.O. Box 3707 Weattle, WA 98124-2207 Basant K. Parida National Aerospace Labs Post Bag No. 1779 Bangalore, 560 017 INDIA




Prof. Judy Schneider Mississippi State University Department of Mechanical Engineering 210 Carpenter Engineering Bldg. P.O. Drawer ME Mississippi State, MS 39762-5925 Prof. Steven Danewicz Mississippi State University Department of Mechanical Engineering 210 Carpenter Engineering Bldg. P.O. Drawer ME Mississippi State, MS 39762-5925 Robert Diamond MARTEST 1245 Hillsmith Dr. Cincinnati, OH 45215 Phil Bretz METCUT Research Inc. 3980 Rosslyn Dr. Cincinnati, OH 45209-1196 David Abeln Cincinnati Testing Laboratories 417 Northland Blvd Cincinnati, OH 45240 John Beavers CC Technologies 6141 Avery Rd. Dublin, OH 43016-8761 Jane Runkle McCook Metals 4900 First Avenue McCook, IL 60525-3294 Hubert Doker German Aerospace Center Porz-Wahnheide Linder Hohe D-51147 Koln GERMANY Richard Brazill

Alcoa Technical Center 100 Technical Dr. Alcoa Center, PA 15069-0001 Prof. Ralph Bush United States Air Force Academy USAF Academy, CO 80840 Kevin Walker Defence Science and Technological Org. 506 Lorimer St. Fishermans Bend VIC 3207 AUSTRALIA Malcolm Loveday NPL Materials Centre National Physical Laboratory Queens Road Teddington Middlesex TW11 0LW UNITED KINGDOM Jim Hartman Honeywell Engines and Systems P.O. Box 52181, M/S 302-101 111 S. 34th St. Phoenix, AZ 85034 Edward Stevens NASA Goddard Space Flight Center Carrier Systems Branch, Code 546 Building 5, Room WO34C Greenbelt, MD 20771 Hazem Kioua Bombardier Aerospace 10000 Cargo A-4 St. Montreal International Airport, Mirabel Mirabel, Quebec J7N 1H3 CANADA Markus Lang EADS 81663 Munich GERMANY Mike Sullentrup




Boeing P.O. Box 516 MC S106-6420 St. Louis, MO 63166-0516 Steve Kimmins British Aerospace AIRBUS New Filton House Filton, Bristol BS99 7AR UNITED KINGDOM Dan Lingenfelser Caterpillar Inc. Technical Center, Bldg. K P.O. Box 1875 Peoria, IL 61656-1875 Fabian Orth Edison Welding Institute 1250 Arthur E. Adams Dr. Columbus, OH 43221-3585 Bob Eastin Federal Aviation Administration Los Angles Aircraft Certification Office 3960 Paramount Blvd. Lakewood, CA 90712-4137 Edward Vesely, Jr. IIT Research Institute 215 Wynn Drive, Suite 101 Huntsville, AL 35805 Kevin B. Lease Kansas State University Mechanical and Nuclear Engineering 317 Rathbone Hall Manhattan, KS 66506-5205 Dale Ball Lockheed Martin Tactical Aircraft Systems Post Office Box 748 Mail Zone 8862 Fort Worth, TX 76101 Frank Stokes ATLSS Research Center Lehigh University

117 ATLSS Drive Bethlehem, PA 18015 Royce G. Forman NASA Johnson Space Center Code EM2/Materials Technology Branch Houston, TX 77058 Roy Hewitt National Research Council of Canada Structures, Materials and Propulsion Lab Montreal Road Ottawa, Ontario K1A 0R6 CANADA Stig Berge Norwegian Univ. of Science and Tech. Faculty of Marine Technology Department of Marine Structures N-7034 Trondheim NORWAY Robert L. Tregoning US Nuclear Regulatory Commission Mail Stop T10 E10 Two White Flint North 11545 Rockville Pike Rockville, MD 20852 Skip Grandt Purdue University School of Aeronautics and Astronautics 1282 Grissom Hall West Lafayette, IN 47907-1282 Ben Hillberry Purdue University School of Mechanical Engineering West Lafayette, IN 47907 Bruce Miglin Shell E&P Technology Westhollow Technology Center P.O. Box 1380 Houston, TX 77251-1380 Cameron Lonsdale Standard Steel




500 N. Walnut St. Burnham, PA 17009 Prof. Mohan Ranganathan University Francois-Rabelais (Tours) Laboratory of Mecanique et Rheologie 7 Avenue Marcel Dassault B.P. 0407 37204 Tours Cedex 3 FRANCE Prof. Pete Laz University of Denver Department of Engineering 2390 S. York St. Denver, CO 80208 Prof. Greg Glinka University of Waterloo Mechanical Engineering Department Waterloo, Ontario NZL 3G1 CANADA Prof. Mike Sutton University of South Carolina Department of Mechanical Engineering Columbia, SC 29208 Prof. David Smith University of Bristol Department of Mechanical Engineering Queens Building, University Walk Bristol BS8 1TR ENGLAND Prof. Rob Ritchie University of California at Berkeley Dept. of Materials Science and Mineral Eng. Materials Sciences Division, MS 62-203 Lawrence Berkeley National Laboratory Berkeley, CA 94720 Prof. Bob Dexter University of Minnesota Department of Civil Engineering Institute of Technology 122 CivE 500 Pillsbury Drive S.E.

Minneapolis, MN 55455-0116 Prof. James Baldwin The University of Oklahoma School of Aerospace and Mechanical Eng. 865 Asp Avenue Norman, OK 73019-0601 Prof. Rick Link US Naval Academy Mechanical Engineering Dept. 590 Holloway Rd. Annapolis, MD 21402-5042 Prof. Norm Dowling Virginia Polytechnic Institute Dept. of Eng. Science and Mechanics Blacksburg, VA 24061 Ian Sinclair University of Southampton School of Engineering Sciences Highfield Southampton SO17 1BJ UNITED KINGDOM Prof. John Yates The University of Sheffield Department of Mechanical Engineering Mappin St. Sheffield S1 3JD UNITED KINGDOM


ASTM E647 FCG Round Robin SwRI October 2004

1

ASTM E647 ROUND-ROBIN: FATIGUE CRACK GROWTH RATE MEASUREMENT INTRODUCTION

When E647 first evolved in the early to mid 1970’s, an extensive round-robin testing effort was performed to assess precision and variability issues both within and between laboratories. The precision and variability statements in E647 are based on this work. Over the past 30 years, gradual changes have occurred with regard to (a) the body of E647, (b) crack length measurement capability, (c) K-control test strategies and (d) servohydraulic control technology. The ASTM E647 fatigue crack growth (FCG) standard is a mature standard: the current version (E647-00) includes over 40 pages and more than 100 external reference citings. There are a number of currently acceptable paths, utilizing different load control methods for instance, allowed for generating FCG data. The intent of the round-robin herein is to control material type and allow as many labs as possible to generate fatigue crack growth rate data for the material(s) of interest using whatever method they desire as long as it falls within the ASTM E647 standard. In order to achieve the goal of this round-robin, a number of specimen have been fabricated from three materials (7075, 2024 and 4130 steel) in two geometries. Testing will be performed primarily at low load ratio with a limited number of tests at higher load ratio. RULES FOR THE ROUND-ROBIN The fundamental rules involved in this round-robin are few and simple:

• Rule #1. Any method allowed by ASTM E647-00 to generate FCG data is allowed (e.g. the organizers are not confining testing to one approach, for instance).

• Rule #2. You must test the specimens that you are supplied with under the conditions

indicated.

• Rule #3. Performing testing to the ∆K regime indicated for the materials.

• Rule #4. Fully document your results and procedures so that we can analyze the data and understand the results (this is really critical as we are going to have quite a bit of data).

These rules should help you refine the parameters associated with testing. The additional information provided below should also assist you in the selection of the conditions for the testing you are performing as part of this round-robin.


2

MATERIALS AND SPECIMENS Three materials have been procured for this testing: two aluminum and one steel alloy. The details of the materials and strength levels are shown in Table 1. We know that load ratio and material type will both impact the measured FCG data and the variability associated with the data. Therefore, these variables will be perturbed during testing.

Table 1. Materials involved in the FCG round-robin including the strengths of the conditions.

Alloy or Temper or Product Product Average Tensile Properties

Material Condition Size Extent σTS, ksi σYS, ksi elong, %

4130 norm-HTed 4” x ¼” bar 36 ft 175 167 17

7075 -T6 0.125” sheet 4’ x 8’ 83 76 14

2024 -T351 0.375” sheet 4’ x 8’ 72 56 20 The raw material was machined into C(T) and M(T) specimen geometries. The dimensions of the specimens varied as indicated in Table 2. Note that Table 2 also indicates specimen ID’s corresponding to the different materials and geometries. The focus was to machine a sufficient number of specimen with enough diversity to fit into the widest range of laboratory facilities. Some of the specimens have initial notches machined into them and some require notching prior to precracking.

Table 2. Specimen ID’s for different materials and specimen geometries.

Material Thickness Specimen Width No. of Specimen

B, inch Geometry W, inch Spec Identification No’s

4130 steel ∼0.25 C(T) 2.0 24 2xy where x = A-K, y = 1-4

C(T) 3.0 25 3xy where x = A-K, y = 1-4

2024-T351 ∼0.25 C(T) 2.0 30 W2-2-x where x = 1-30

C(T) 3.0 30 W3-2-x where x = 1-30

∼0.375 M(T) 4.0 32 AL-2- x where x = 1-32

7075-T6 ∼0.125 M(T) 4.0 44 AL-7- x where x = 1-44


3

FCG TEST CONDITIONS REQUIRED The testing that will ultimately be performed by a given lab is a function of a number of variables inherent in that laboratory. These variables include, for instance, load capacity of the machine and/or fixture, initial notch size and shape and test control method (constant amplitude versus K-control). The intention in this round-robin is not to dictate the test conditions but rather to allow the user to choose, within of course the context of E647. Shown in Table 3 are the conditions that will be evaluated during the round-robin. Note that the last column identifies the condition that the organizers are asking you to test which was identified in your shipment of specimen. Not all testing organizations will be testing the same conditions. However, as a general rule the low load ratio behavior will be the focus of the vast majority of the testing.

The envelope of growth rates indicated in Table 3 are provided for your guidance only in terms of selecting the conditions for testing. Achieving the full range of growth rate data indicated by the upper and lower bound values in Table 3 is unlikely given the specimen sizes involved in testing. Therefore, when selecting your testing conditions consider the following:

• For low load ratio testing (R=0.1), the focus is on the growth rates starting at ∆K = 10 ksi√in

• For higher load ratio testing (R>0.1), toughness limitations require generally starting lower, on the order of ∆K = 8 ksi√in or so.

Do the best you can in choosing conditions based on these limitations. Just remember: when in doubt, follow E647! Table 3. Generalized test conditions for the round robin testing including approximate lower and upper bound envelope of crack growth rates and applied stress intensity factor ranges. Material Thickness Load Lower Bound Upper Bound Round-Robin

B, inch Ratio ∆K, ksi√in

da/dN, inch/cyc

∆K, ksi√in

da/dN, inch/cyc

Condition ID

4130 steel ∼0.25 0.1 8 1 (10-7) 30 6 (10-6) STL-A

0.8 8 3 (10-7) 30 1 (10-5) STL-B

2024-T351 ∼0.25 0.1 5 2 (10-7) 40 1 (10-3) ALX-A

0.5 5 1 (10-6) 20 1 (10-4) ALX-C

∼0.375 0.1 5 2 (10-7) 40 1 (10-3) ALX-B

7075-T6 ∼0.125 0.1 5 3 (10-7) 40 1 (10-3) ALN-A


4

REQUIRED DOCUMENTATION With the relatively open philosophy (e.g. not confining testing to certain techniques and methods) used during this testing, the burden when evaluating the results will be to understand how the testing was performed so as to potentially determine the roles of different variables. This means that there is additional burden on the testing lab to provide sufficient, highly detailed information to the round-robin organizers. To assist in this process, Attachment A is provided to guide in the process of documenting the procedures followed. All of the information in Attachment A is important to include with the data generated, and we request that you please provide it when you supply the data to the round-robin organizers. Keep in mind that we need complete details regarding how the specimen was precracked prior to starting the test. Once the test begins, details regarding how it was loaded are then critically important. The analysis procedures used with the data are also important to document. You need to make sure that the data you have provided is sufficient for us to re-process the data, if necessary. So, instead of simply providing da/dN and ∆K (which in the strictest sense is the minimum data required), we also need crack length, cycle count and applied load information. This is important information should it be necessary to re-process all of the data that you provided. If non-visual crack length measurement is performed, please be sure to indicate what type of post-test correction strategy was utilized to relate the visual to the non-visual crack measurement data. A comparison between the visual measurements and post-test corrected data would also be highly useful. Be sure to include all elements of what E647 requires in terms of reporting data in addition to that recommend in Attachment A. CONTACT INFORMATION Please do not hesitate to contact any of the round robin organizers if you have any additional questions. Primary: Secondary:

Pete McKeighan Jim Feiger Southwest Research Institute Southwest Research Institute San Antonio, TX San Antonio, TX 210.522.3617 210.522.6881 [email protected] [email protected]


5

ATTACHMENT A – Information Required in Test Documentation

1) Fatigue Test Lab Information

a) Location b) Technician(s)

2) Testing Equipment and Setup

a) Test frame type b) Test frame capacity c) Test control hardware/software d) Test Specifics

i) Load cell range/calibration ii) Crack length determination

(1) Compliance (a) clip gage calibration (b) clip gage gage length

(2) Potential drop (a) System used (DCPD/ACPD)

(i) Indirect or Direct (ii) Current Magnitude

(b) Probe geometry (i) Single or dual probe (ii) Location and dimensions

(3) Visual techniques (i) Device and equipment (ii) Resolution

iii) Environmental Conditions (1) Temperature (°C) (2) Humidity (%) (3) Test Date (day/month/year) / duration (days)

iv) Grips (1) Clevis size (C(T) geometry) (2) Grip configuration (M(T) geometry)

(a) Grip-to-grip distance (b) Bending verified (strain gages)

3) Specimen Details

a) Material i) Steel

(1) Modulus used for crack length compliance (if used)

ii) Aluminum (1) Two alloys evaluated (2) Modulus used for crack length determination

(if used) b) Specimen Geometry

i) C(T) fatigue crack growth specimen (1) Two sizes: W=2-in and 3-in

ii) M(T) fatigue crack growth specimen (1) E647 stress intensity solution ? (2) 2W= 4-in

iii) Dimensions (1) W (width), B (thickness) (2) Notch length (3) Notch height (4) Crack length transducer location and

dimensions iv) Specimen preparation

(1) Polishing, etc.

v) Notch type (1) EDM, slitting saw, chevron, other

vi) Alteration(s) to specimen from the as-received condition

4) Test Procedure Details

a) Precracking i) Overall precracking procedures

(1) Loading shedding, constant ∆K, etc. (2) FCGR at end of precracking

ii) Loading conditions for precracking (1) Pmax, R-ratio (2) Frequency (3) Initial and final crack lengths (4) Initial and final ∆K levels

iii) Crack tip symmetry: front/back and left/right (M(T))

b) FCG Testing i) Approach

(1) Constant amplitude – force function curve type (a) Loading conditions: Pmax, R-ratio,

frequency (b) Initial and final crack lengths (c) Initial and final ∆K levels (d) ∆a/W crack length interval for data points (e) Visual crack length intervals and number

of visual taken (f) Crack tip symmetry: front/back and

left/right (2) K-control

(a) Initial stress intensity (b) K-gradient (c) R-ratio, frequency (d) Initial crack length, final crack length (e) ∆a/W interval for data collection (f) crack tip symmetry: left/right and

front/back (3) Other

(a) Describe overall technique 5) Analysis Technique (post-test processing)

a) Automated/manual b) Method used

i) Polynomial ii) Secant iii) Other

c) Crack front profile, final crack length measurement d) Error: visual crack lengths versus transducer determined e) Anomalies during testing

i) Power outages ii) Hold times iii) Change in environement iv) Other

f) Fracture surface appearance/anomalies i) Crack in plane?

g) Validation of yield criteria for both specimen geometries i) Provide yield strength to participants

B-1

Appendix B – Individual Lab Data Plots

B-2


B-3

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab A (2F3)

Material:B:

Geometry:R:


B-4

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab A (2F4)Lab A (3B1)Lab A (3B2)

Material:B:

Geometry:R:


B-5

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab A (W2-2-5)Lab A (W2-2-6)Lab A (W2-2-7)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.1

B-6

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab B (3C4)Lab B (3E1)

Material:B:

Geometry:R:


B-7

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab B (3E4)Lab B (3F1)

Material:B:

Geometry:R:


B-8

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab B (W3-2-10)Lab B (W3-2-11)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.1

B-9

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab B (W3-2-14)Lab B (W3-2-15)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.5

B-10

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab B (AL-2-10)Lab B (AL-2-11)

Material:B:

Geometry:R:

2024-T3510.375-inchM(T)0.1

B-11

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab B (AL-7-9)Lab B (AL-7-10)

Material:B:

Geometry:R:

7075-T60.125-inchM(T)0.1

B-12

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab C (2K1 K-decrease)Lab C (2K1 K-increase)Lab C (2K2 K-decrease)Lab C (2K2 K-increase)

Material:B:

Geometry:R:


B-13

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab C (W2-2-23 K-decrease)Lab C (W2-2-23 K-increase)Lab C (W2-2-24 K-decrease)Lab C (W2-2-24 K-increase)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.1

B-14

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab C (W3-2-22 K-decrease)Lab C (W3-2-22 K-increase)Lab C (W3-2-23 K-decrease)Lab C (W3-2-23 K-increase)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.5

B-15

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4


Material:B:

Geometry:R:

2024-T3510.375-inchM(T)0.1

B-16

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2


Material:B:

Geometry:R:

7075-T60.125-inchM(T)0.1

B-17

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab E (3L1)Lab E (3L2)

Material:B:

Geometry:R:


B-18

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab E (W2-2-29)Lab E (W2-2-30)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.1

B-19

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab E (AL-2-20)Lab E (AL-2-21)

Material:B:

Geometry:R:

2024-T3510.375-inchM(T)0.1

B-20

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab E (AL-7-30)Lab E (AL-7-31)

Material:B:

Geometry:R:

7075-T60.125-inchM(T)0.1

B-21

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab F (2A1)Lab F (2B1)Lab F (3A1)Lab F (3A2)

Material:B:

Geometry:R:


B-22

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab F (3A3)Lab F (3A4)

Material:B:

Geometry:R:


B-23

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab F (W3-2-1)Lab F (W3-2-2)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.1

B-24

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab F (W3-2-3)Lab F (W3-2-4)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.5

B-25

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab F (AL-2-1)Lab F (AL-2-2)

Material:B:

Geometry:R:

2024-T3510.375-inchM(T)0.1

B-26

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab F (AL-7-1)Lab F (AL-7-2)

Material:B:

Geometry:R:

7075-T60.125-inchM(T)0.1

B-27

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab G (3B3)Lab G (3B4)Lab G (3C1)

Material:B:

Geometry:R:


B-28

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab G (3C2)Lab G (3C3)

Material:B:

Geometry:R:


B-29

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab G (W3-2-5)Lab G (W3-2-6)Lab G (W3-2-7)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.1

B-30

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab G (W3-2-8)Lab G (W3-2-9)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.5

B-31

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab G (AL-7-7)Lab G (AL-7-8)

Material:B:

Geometry:R:

7075-T60.125-inchM(T)0.1

B-32

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab H (2J1 const. amp.)Lab H (2J2 const. amp.)Lab H (2J1 K-decrease)Lab H (2J2 K-increase)

Material:B:

Geometry:R:


B-33

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab H (W2-2-15 const. amp.)Lab H (W2-2-16 const. amp.)Lab H (W2-2-15 K-decrease)Lab H (W2-2-16 K-decrease)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.1

B-34

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab H (W2-2-17 const. amp.)Lab H (W2-2-18 const. amp.)Lab H (W2-2-17 K-decrease)Lab H (W2-2-18 K-decrease)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.5

B-35

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

Lab I (2G1 K-increase)Lab I (2G2 K-increase)Lab I (2G1 K-decrease)Lab I (2G2 K-decrease)

Material:B:

Geometry:R:


B-36

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

Lab I (2G3 K-increase)Lab I (2G4 K-increase)Lab I (2G3 K-decrease)Lab I (2G4 K-decrease)

Material:B:

Geometry:R:


B-37

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

Lab I (W2-2-8 K-increase)Lab I (W2-2-9 K-increase)Lab I (W2-2-8 K-decrease)Lab I (W2-2-9 K-decrease)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.1

B-38

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab I (W2-2-10 K-increase)Lab I (W2-2-11 K-increase)Lab I (W2-2-10 K-decrease)Lab I (W2-2-11 K-decrease)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.5

B-39

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2


Material:B:

Geometry:R:

2024-T3510.375-inchM(T)0.1

B-40

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2


Material:B:

Geometry:R:

7075-T60.125-inchM(T)0.1

B-41

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab J (2I1)Lab J (2I2)

Material:B:

Geometry:R:


B-42

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab J (2I3)Lab J (2I4)

Material:B:

Geometry:R:


B-43

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab J (W2-2-12)Lab J (W2-2-12)Lab J (W2-2-14)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.1

B-44

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab K (3F2)Lab K (3G1)

Material:B:

Geometry:R:


B-45

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

10-1

Lab K (W3-2-16B)Lab K (W3-2-28B)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.1

B-46

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab K (W3-2-17B)Lab K (W3-2-29B)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.5

B-47

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab L (W3-2-18)Lab L (W3-2-19)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.1

B-48

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab L (W3-2-20)Lab L (W3-2-21)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.5

B-49

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab L (AL-2-12)Lab L (AL-2-13)Lab L (AL-2-14)

Material:B:

Geometry:R:

2024-T3510.375-inchM(T)0.1

B-50

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

LabL (AL-7-11)Lab L (AL-7-12)Lab L (AL-7-13)

Material:B:

Geometry:R:

7075-T60.125-inchM(T)0.1

B-51

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

Lab M (AL-2-29)Lab M (AL-2-30)Lab M (AL-2-33)

Material:B:

Geometry:R:

2024-T3510.375-inchM(T)0.1

B-52

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab M (AL-7-32)Lab M (AL-7-33)Lab M (AL-7-34)

Material:B:

Geometry:R:

7075-T60.125-inchM(T)0.1

B-53

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab N (3K1)Lab N (3K2)

Material:B:

Geometry:R:


B-54

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab N (3I1)Lab N (3I2)

Material:B:

Geometry:R:


B-55

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab O (2J4)Lab O (2J3)

Material:B:

Geometry:R:


B-56

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab O (W2-2-19)Lab O(W2-2-20)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.1

B-57

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab O (W2-2-21)Lab O (W2-2-22)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.5

B-58

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab P (2C1)Lab P (2E1)

Material:B:

Geometry:R:


B-59

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab P (2F1)Lab P (2F2)

Material:B:

Geometry:R:


B-60

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

Lab P (W2-2-1)Lab P (W2-2-2)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.1

B-61

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab P (W2-2-3)Lab P (W2-2-4)

Material:B:

Geometry:R:

2024-T3510.25-inchC(T)0.5

B-62

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

Lab P (AL-2-3)Lab P (AL-2-4)

Material:B:

Geometry:R:

2024-T3510.375-inchM(T)0.1

B-63

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab P (AL-7-3)Lab P (AL-7-4)

Material:B:

Geometry:R:

7075-T60.125-inchM(T)0.1

B-64

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab Q (001 K-decrease)Lab Q (001 K-increase)Lab Q (301 K-decrease)Lab Q (301 K-increase)

Material:B:

Geometry:R:

2024-T3510.375-inchactually C(T)0.1

B-65

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab R (AL-2-25)

Material:B:

Geometry:R:

2024-T3510.375-inchM(T)0.1

B-66

ΔK, MPa√m10 100

da/d

N, m

/cyc

le10-9

10-8

10-7

10-6

10-5

10-4

ΔK, ksi√in1 10 100

da/d

N, i

n/cy

cle

10-8

10-7

10-6

10-5

10-4

10-3

10-2

Lab R (AL-7-18)Lab R (AL-7-19)Lab R (AL-7-20)

Material:B:

Geometry:R:

7075-T60.125-inchM(T)0.1

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[Date RR # approved – ASTM to assign] · 2013-08-28 · [Date RR # approved – ASTM to assign]...

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Transcript of [Date RR # approved – ASTM to assign] · 2013-08-28 · [Date RR # approved – ASTM to assign]...