An Unified Approach to Structural Health Management and Damage Prognosis of Metallic

Aerospace Structures

Prognosis of Aircraft and Space Devices, Components, and SystemsAir Force Office of Scientific Research

University of Cincinnati, Cincinnati, Ohio

February 19 and 20, 2008

Grant Number: FA95550-06-1-0309

Program Manager: Dr. Victor Giurgiutiu

Aditi Chattopadhyay

Department of Mechanical and Aerospace Engineering

Arizona State University

2

MURI

Structural Health Monitoring & Prognosis of Aerospace Systems

Douglas Cochran•Statistical signal processing•Theory of sensing•Mathematical modeling

James B. Spicer•Materials process monitoring & control

•Ultrasonics

•High-temperature characterization

Dan Inman•SHM

• Wireless sensing and damage assessment

•Membrane optics

Roger G. Ghanem•Risk assessment•Stochastic mechanics•Computational mechanics•Inverse problems and optimization

Antonia Papandreou•Signal processing •Sensing & Information Processing •Detection & Estimation

Aditi Chattopadhyay•Smart structures

•SHM

•Multiscale modeling

•Mechanics of composites

•MDO

Pedro Peralta

•Fracture & fatigue •Composite materials •FEM •Continuum mechanics

MURI RESEARCH TEAMASU TeamAcademic Professionals: Jun Wei, Narayan Kovvali

Graduate Students : Debejyo Chakraborty, Clyde Coelho, Chuntao Luo, Subhasish Mohanty, Donna Simon, Sunilkumar Soni, Rikki Teale, Christina Willhauck

USC TeamAcademic Professional: Maarten Arnst

Graduate Students : Maud Comboul, Sonjoy Das, Arash Noshadravan

JHU TeamAcademic Professional: Seyi Balogun

Graduate Students : Lindsay Channels, Travis DeJournett

VT TeamAcademic Professional: Benjamin L. Grisso

Graduate Student : Mana Afshari

BENEFIT TO DOD/INDUSTRY TECHNICAL APPROACH

•Physically based models to characterize damage nucleation & growth

•Characterize wave propagation in hotspot

•Optimally integrate sensor network

•Waveform design & damage detection

•Sensor management schemes for detection/classification

•Stochastic models to account for uncertainties

•Estimation of remaining useful life

•Validation on test structures

• Connect microscopic damage to macroscopic scale monitoring

• Sensor sensitivity

• Sensor/host structure coupling

• Hierarchical information management

• Hybrid approach for life estimation

• Precursor to damage/first failure to inspection

OBJECTIVES•Computationally efficient multiscale modeling techniques for characterizing the damage state of a material (including nucleation and growth)

• Damage detection and classification techniques for sensor integration and instrumentation

• Prognosis capabilities for predicting failure probability and remaining useful life

• Testing, validation and application

BASIC RESEARCH ISSUES

• Improved techniques will facilitate assessment of health of metallic aircraft structures

• Project outcome will help surmount some of the technical challenges, complementing ongoing activities at AFRL

• Research results will help establish improved IVHM systems

• Future aircraft systems can benefit from integration with prognosis programs focused on current aircraft.

• Advancements in damage analysis, detection & classification are useful sustainable infrastructure and electronic system monitoring

TASK DESCRIPTIONS AND PERSONNEL

A. ChattopadhyayP. Peralta

Roger Ghanem

Task 1•Material Charecterization•Multiscale model to predict damage nucleation & growth

Task 2•Optimal sensor placement•Detection•Signal processing•Diagnosis & classification

Task 3•State awareness •Life prediction

Task 4 (All PI’s)Testing, validation and

applications

A. ChattopadhyayP. Peralta

James Spicer

A. ChattopadhyayA. Papandreou-Suppappola

Daniel InmanDouglas Cochran

AFRL / VA Boeing Phantom Works

DOD COLLABORATIONS AND TRANSITION TO REAL SYSTEMS

• Collaboration with AFRL: • Mark Derriso, Structural Health Assessment Team Leader, AFRL/VA

• Provides data from AFRL experimental set-ups• Frequent meetings with Mark and his team: discuss MURI progress

and relevant AFRL problems needed to help transition of our work to real systems

• Meetings with Jim Larsen (AFRL/MLLMN) and Kumar Jata (AFRL/MLLP) • Collaboration with Boeing Phantom Works (Eric Haugse)

• Hotspot Program with AFRL (involves actual F-18 testing in Arizona for transition to real systems).Participants: AFRL, Boeing Phantom Works, Accelent Technologies, Metis Design

• HotSpot Monitoring Program teleconference (bi-weekly)• Advisory board committee provides feedback:

• Members from AFRL, US Air Force Academy, United Technologies Research Center, Boeing, Next Generation Aeronautics, Lockheed Martin Aeronautics Company, National Transportation Safety Board, NRL, Naval Surface Warfare Center, NASA GRC, NASA LaRC, NASA ARC, US Army ARDECOM, Los Alamos National Lab.

RVE for Grains/ Particles3D Grain/ Particle size

distributions

Material

Characterization

Multiscale

ModelingMicrostructure Reconstruction

Short Crack Growth in the Mesoscale

Structure Level Fatigue Simulation

Representative Microstructure (FEM)

Metallography Microscale

Damage Initiation

Physically-based Multiscale Modeling

TECHNICAL APPROACH

Strain fields ahead of fatigue cracks in wrought Al alloys: in-situ testing and DIC

Nanoindentation of precipitates in wrought Al alloys

Load stage

Loaded specimen

Load Direction and Rolling Direction (RD)

MATERIALS CHARACTERIZATIONMultiscale Material Characterization

Crack tipCrack tip

Use Electron Backscatter Diffraction (EBSD) along with serial sectioning: 2-, 2.5- and 3-D

“Artificial” microstructures are also being generated

Same grain size (100 µm) different grain size distribution Large (300 µm) grain size

2.5-D 3-D

MATERIALS CHARACTERIZATIONMicrostructure Reconstruction and Representation

2-D

Use microstructure representation and meshing tools: defects can be included

Results show effects of microstructural variability on local fields

2.5-D 3-D

MATERIALS CHARACTERIZATIONMicrostructurally Explicit Finite Element Models

2-D

2-D

3-D

11

INTERACTION OF RELEVANT SCALES IN MULTISCALE MODELING

2-D Slice

2.5-D Representation

3-D Representation

Material Characterization

Void Model

Single Crystal Structure

Micro Scale

Crystal Orientation

Crystal Properties

Orientation Distribution &

Properties

Short Crack Propagation

Meso Scale

Crack Initiation

Polycrystal Structure

Homogenization

Localization

Wave Propagation

Long Crack Propagation

Macro Scale

Component

Damage Parameter

0. 00

0. 01

0. 01

0. 02

0. 02

0. 03

0. 03

0. 00 5. 00 10. 00 15. 00 20. 00 25. 00 30. 00 35. 00

Time (s)

Dam

age

Par

amet

er

0

50

100

150

200

250

300

0 0.002 0.004 0.006 0.008 0.01

Strain

Str

ess

(MP

a)

Hardening Parameter

12

-400

-300

-200

-100

0

100

200

300

400

-0.004 -0.003 -0.002 -0.001 0 0.001 0.002 0.003 0.004

0.0E+00

2.0E-02

4.0E-02

6.0E-02

8.0E-02

0 2 4 6 8

Mises stress distributionStress-strain response

FATIGUE ANALYSIS (SINGLE CRYSTAL)

Str

ess

(MP

a)

Strain

Acc

um

ula

ted

sh

ear

stra

in

Number of cycles

Capture crystal orientation

Fatigue hardening & saturation

Accumulative shear strain

Mesoscale

13

-4 -3 -2 -1 0 1 2 3 4

Strain X103

-500

-200

-300

-400

-100

100

0

200

300

400

500

Str

es

s (

MP

a)

Grains

OIM (Orientation Imaging Microscopy)

Scan

MESOSCALE STRUCTURE

OOF

ABAQUS & UMAT

Methods for In Situ Interrogation and Detection

Sensor design, network and placement

Damage detection and classification

Nonlinear ultrasonicdamage characterization

Sensing multi-scale damage with impedance, vibration,

& Lamb wave based methods

Time-frequency & statisticaldamage classification: AFRL TPS, ASU bolted-joint data

Mesoscopic ultrasonic techniques for assessment of material microstructure

FEM based analysis of macro- length scale damage

with virtual sensors

Bayesian sensor fusion of data received from

multiple distributed sensors

TECHNICAL APPROACH

-0.00636

-0.00477

-0.00318

-0.00159

0

-0.05

-0.04

-0.03

-0.02

-0.01

0

0 400 800 1200 1600

Am

plit

ude o

f M

od

el

Am

plitu

de o

f Un

fatig

ued 6

06

1

Time (ns)

Experimental Schematic for Laser Ultrasonic Investigations

Nd:YAG 1064 nm 9 ns pulse

Iris

Sample on translation stage

Nd:YAG532 nmcontinuous

Stabilization circuit

Lens

Piezoelectric mirror mount

Oscilloscope

Ultrasonic generation

2.4 mJ

Receiver

Lens

Mirror

Michelson type interferometer

+

_

Surface displacement

ASU SP Group (Papandreou, Cochran)

JHU Ultrasonics Group (Spicer)

data

Ultrasonic displacement measured at the epicenter

Model

Measurement

Spectrogram

Team Integration

RESULTS: NONLINEAR ULTRASONICS

ResultsSample Tested

Lug joint: typical structural hot spot

Actuator

Sensor

ASU SP Group (Papandreou, Cochran)

ASU Modeling Group (Chattopadhyay)

features for SVM

Team Integration

RESULTS: SUPPORT VECTOR MACHINE BASED DAMAGE CLASSIFICATION

Collaboration with Mark Derriso (AFRL/VA)

• Damage Class definition: - Class 1 = Bolt 1 at 25%

torque- Class 2 = Bolt 2 at 25%

torque - Class 3 = Bolt 3 at 25%

torque- Class 4 = Bolt 4 at 25%

torque- Class 5 = All bolts at 100%

torque (fully tightened/healthy case)

• PZTs attached to bolted square aluminum plate

• PZT-1 used for transmitting 0-1.5 kHz chirp

• Signals received at PZT-2, PZT-3, and PZT-4

S. Olson, M. DeSimio, and M. Derriso, “Fastener Damage Estimation in a Square Aluminum Plate”, Structural Health Monitoring Journal, 2005

Confusion matrix (HMM based damage classifier)

0.80500 0.05500 0.13500 0 0.00500

0 0.98000 0.00500 0 0.01500

0.05500 0.08000 0.86500 0 0

0.00500 0.02000 0 0.97000 0.00500

0 0 0 0.00125 0.99875

RESULTS: TIME-FREQUENCY CLASSIFICATION

Probabilistic Data Driven Prognosis Model

Fracture Mechanics Based Physics Model

System Identification Prognosis Model

Prediction of crack growth and plastic zone parameters by

Gaussian Process Model

Prediction of effective stress intensity factors that account

for closure effects

Vibration and wave based system identification for damage state estimation

R

U

L

E

Hybrid

Prognosis Model

Prognosis via State-Awareness and Life Models

TECHNICAL APPROACH

Flight Cycle

Dam

age

Ind

ex (

Cra

ck L

eng

th)

k N N+1

GAUSSIAN PROCESS DATA DRIVEN APPROACH• Based on high dimensional kernel function

• Uncertainty quantified using Bayesian approach

• History as training distribution

• Predicts new mean damage and associated variance

• Predicts possible collapse point if new predicted variance

exceeds threshold flag

DATA DRIVEN MODEL PREDICTION

Single Variate Model

Prediction

Load Spectrum

HYBRID PROGNOSIS APPROACH

Data driven model for calculating

plastic zone constraint factor

Incremental crack length from

physics based model

Results From Pure Physics Based Model Results From Hybrid Model

•Detection•Signal processing•Diagnosis & classification

Material characterization, multiscale model and state awareness model

Testing, Validation & Application

Calibrate and validate modeling methods

Sensor network and placement

Application to Structural Hotspots

AFRL/VA, Boeing Structural Hotspot

Program

TECHNICAL APPROACH

Sample 1 (Polished) Sample 2 (Sand Blasted) Sample 3 (Sand Blasted)

Life of sample 2 about twice of sample 1 under similar loading condition

Two distinct damage nucleation sites for sample 2

Failure mode - High cycle fatigue from shoulders for sample 1 & 2

- Very high cycle fretting fatigue from pin hole

Induced Stresses Influence Fatigue Life and Failure Patterns

EXPERIMENTAL OBSERVATIONS

380,621 823,537 >3 MillionCycles to failure (110 – 1100 lbs) (80 – 800 lbs)(110 – 1100 lbs)

GUIDED WAVE IN LUG JOINT

Healthy

Damaged

FUTURE WORKTask 1:• Predict damage nucleation & propagation using modified fatigue damage criteria.• Simulate sensor signals & study their interaction with cracks using distributed point

source method (DPSM) – a wave based approach.

Task 2:• Adaptive signal processing and classification using active and multi-task learning

methodologies.• Use of data from new sensors and physics based FEM modeling to train damage

detection and classification algorithms.

Task 3:• Formulate multivariate prognosis models that incorporate physical-based models to

account for load sequence effects.• Incorporate material and sensor signal variability into prognosis framework.• Develop a prognosis approach for crack nucleation based on "virtual sensors" (output

from multiscale modeling) to estimate life spend to grow "detectable" damage.

Task 4:• Perform testing on instrumented samples with complex geometry (lug joints, bolted

joints) to gather statistical information on failure modes, sensor performance and to collect data for model validation (integration with Tasks 1, 2, and 3).

• Develop a test article for use with the biaxial load frame to obtain statistical information under both complex geometries and complex loading (integration with Tasks 1, 2, and 3).