QU7 Optimization of Nitriding Experiments Using Taguchi Methodologies From QE

THE ANNALS OF “DUNAREA DE JOS” UNIVERSITY OF GALATI

FASCICLE IX METALLURGY AND MATERIALS SCIENCE, ISSN 1453 – 083X NR 2 – 2004

OPTIMIZATION OF NITRIDING EXPERIMENTS USING TAGUCHI METODOLOGIES FROM QE

Nelu CAZACU*, Sorin DOBROVICI*, Adolf BACLEA**, Elena DRUGESCU*,

*“Dunarea de Jos” University of Galati

** S.C. Cosena S.R.L Constanta, Romania e-mail: [email protected]

ABSTRACT

Decreasing costs of experiments became an important target when number of

influence factors is high. Classical experimental procedure (full factorial) conducing to many lines in experiment matrix. Some partial factorial procedures are used in these situation. For technological experiments like thermochemical treatments Taguchi methodologies (Quality engineering - QE) having a most important properties: the numbers of lines in experimental matrix having a drastic decreasing based by orthogonal array and its mathematical mode are applicabll. Taguchi methodologies were applied for fluidized bed nitriding. Experiments were made using an experimental FBT furnace and processes was conduced by orthogonal array methodology for evaluating the values of various factor influence over process (product).

KEYWORDS: nitriding, fluidized bed, orthogonal array.

1. Introduction

Statistical process control (SPC) is used to

ensure that processes are meeting the standard, but low costs for product are based by establishing an optimal parameter design [3]. Quality engineering introduces a new terms and new manner for treating product quality.

Quality Robust Method calls for making products and processes that are quality robust. Quality robust products are products that can be produced uniformly and consistently in a variety of adverse manufacturing and environmental conditions. The basic idea is to remove the effects of adverse condition instead of removing the causes [4]. In this way small variations in materials and process do not destroy product quality. In this manner products can be produced more uniformly and will perform more consistently in service under a variety of conditions.

Quality Loss Function (QLF) identifies all costs connected with poor quality and shows these costs increase as the product moves away from being exactly what the customer wants [3]. All the losses to society due poor performance of a product are included in the loss function. The smaller loss is target desirable for the producer. The farther the product is from the target value the more severe the loss [3]. Target value. Traditional way of looking at specifications (that is, the product is good until it fails to fall within the tolerance limits) is too simplistic. As shown, conformance oriented quality produces more units farther from the target; therefore the loss (costs)

is higher in terms of customer satisfaction and benefits to society. When conducting experiments using an orthogonal array obtaining a low number of experiments [4].

2. Experimental conditions

Nitriding are a common treatment of steels surface for machine industry. After nitriding treatment, superficial layers with special properties (high hardness) simultaneously with a reliable core which will offer to part exploiting exceptional properties (fatigue and wear resistance, tenacity). For increasing quality for treated parts different techniques having a rapid developing, diversification and adaptability to product processes.

fluidized bed nitriding process

fluidization

- granular solid properties- gas properties- geometry of furnace- dimension of furnace

material properties

- chemical composition-shape -solid dimension

nitriding process

- nitriding timenitriding emperature- media activity

Fig. 1. Factors grouping for fluidized bed

nitriding.

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solid properties

thermophysic gas properties

geometric fluidization

space

solid naturesolid dimension

solid formthermal conductivity

thermal conductivityviscosity

specific heatingdensity

high working spaceworking space diameter

working space shapeH/D rapport

Fig. 2. Factors with influence over fluidization experiments, [[5]].

Table 1. Chemical composition of 34MoCrNi15 steel

C Mn Si P S Cu Cr Ni Ti Mo Nb V0,34 0,55 0,27 0,035 0,040 - 1,55 1,55 - - - -

Table 2. Selected factors for experiments and their levels.

UM 1 2 3A austenitisation time min 10 15 20B tempering temperature °C 200 400 600C tempering time min 10 20 30D nitriding temperature °C 475 525 757E nitriding time min 30 60 90F fluidization speed l/h 463 731 1104

Factor Level

Gas nitriding and plasma nitriding are the most used technologies, but investments costs are higher. Fluidization technologies offer a very intensive media for heat and mass transfer and nitriding time having a decreasing. Some shape and dimensional limitations are important for fluidized bed nitriding and for other thermochemical treatments like (carburizing and carbonitriding).

Many factors having influence over fluidized bed nitriding process Fig. 1. and many of this depending if fluidization phenomenon Fig. 2. If all factors are considered for experiments the experiment matrix is too large and costs and time are increasing. Is important to underline that each, factor having a different weight over experiments. For fluidized bed nitriding experiments was used: • fluidized bed furnace (diameter: 140mm, high

over diameter rapport H/D=1) • indirect electrical heating (max 5kW) • solid granular: quartz sand 0,010…0.016mm] • fluidization gas: ammonia • samples from 34MoCrNi15 steel (Table 1)

• experimental procedure based by orthogonal array For conducting nitriding experiment was used an

orthogonal L18 (6 factors x 3 levels), [Table 3]. [4]. We selected six factors and we got tree levels to each one. The experimental matrix is showing in Table 3. Similar “full factorial” nitriding experiments need 729 line in array, [4]. Important for experiment methodologies is established a target function. For a set of experiments different target function is possible to be taken and will be a successive interpretations. The target functions are some properties of product transformed in signal/noise rapport [4], with relation:

)log(10 pn −= (1) where: p is a product measurable property.

3. Results and discussion

For heat treatments experiments over 34MoCrNi15 steel, target function is transformation with relation (1) for nitriding layer depth (combination and total). The influence of each level

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of each factor is showing in Table 4 and graphic representation is showing in Fig. 3 and Fig.4.

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Table 3. Experiments characterization

1 2 3 4 5austenitisation

timetempering

temperaturetempering

timenitriding

temperaturenitriding

timefluidization

speedA B C D E

UM min °C min °C min l/h1 10 200 10 475 30 4632 10 400 20 525 60 7313 10 600 30 757 90 11044 15 200 10 525 60 11045 15 400 20 757 90 4636 15 600 30 475 30 7317 20 200 20 475 90 11048 20 400 30 525 30 4639 20 600 10 757 60 73110 10 200 30 757 60 46311 10 400 10 475 90 73112 10 600 20 525 30 110413 15 200 20 757 30 73114 15 400 30 475 60 110415 15 600 10 525 90 46316 20 200 30 525 90 73117 20 400 10 757 30 110418 20 600 20 475 60 463

Experiment

Specified factors

6

F

Table 4. Calculus for average effect of each level of factors over experiment

1 2 3A austenitisation time -8.56 -5.26 -5.41B tempering temperature -7.37 -5.69 -6.17C tempering time -6.88 -6.26 -6.09D nitriding temperature -5.72 -6.01 -7.50E nitriding time -6.06 -6.58 -6.60F fluidization speed -5.80 -7.10 -6.33

Factor Levels (dB)

-9.00

-8.00

-7.00

-6.00

-5.00

-4.001 2 3

mij

A BC m

Fig. 3. Mean effect of A, B and C factor over experiments.

-9.00

-8.00

-7.00

-6.00

-5.00

-4.001 2 3

levels

mij

D EF m

Fig. 4. Mean effect of D, E and F factor over experiments.

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-14.00

-12.00

-10.00

-8.00

-6.00

-4.00

-2.00

0.00

mFourie Series

Fig. 5. Target function over all experiments (Fourier series).

-10.00

-9.00

-8.00

-7.00

-6.00

-5.00

-4.00

Fact

or A

effe

ct

A m

Fig. 6. Factor A effect over all experiments.

-8.00

-7.50

-7.00

-6.50

-6.00

-5.50

-5.00

Fact

or E

effe

ct

E m

Fig. 7. Factor B effect over all experiments.

Table 5. ANOVA analysis for weight of each influence factor.

- (dB)2 %A austenitisation time 2 41.51 59.33B tempering temperature 2 9.00 12.86C tempering time 2 2.07 2.96D nitriding temperature 2 11.01 15.73E nitriding time 2 1.11 1.59F fluidization speed 2 5.27 7.53

Factormu

factor weightfreedom degrees

square sum

Fig. 8. Microstructure for experiment no.05 (left) and no.16 (right), (100x , etching: nital 2%).

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0.00 10.00 20.00 30.00 40.00 50.00 60.00

austenitisation timeA

tempering temperatureB

tempering timeC

nitriding temperatureD

nitriding timeE

fluidization speedF

Fig. 9. Weight factors over heat treatment (%).

For all factors an influence was study like A and

B. A special statistic instrument (ANOVA) was utilized for weight influence of all six factors over heat treatment (quenching tempering and nitriding), [3], [4]. The results are showing in Table 5.

4. Conclusions

Using orthogonal array is necessary minimal number of experiments. From a measurable properties, mathematical model make necessary a target function (“signal”) with a same definition lake Fourier series. In this conditions main influence of level of each factor are showing. Factor C having a linear influence over experimental interval, factor D an approximative linear influence at the and factor A, B, E and F having an influence with maximum. Microstructures show a normal behavior for considered steel for final nitriding regimes. But not all microstructure having nitriding aspect because time and temperature factors, for some experiments conducting at incomplete surface transformation. Theoretical domain of each factor variation is possible to having a large limits but technological and

experience conducting limits to reasonable engineering values.

In this stage of experiments and interpretation of results the weight (importance) of each factor over heat treatment experiments are showing in Table 5 and Fig. 9.

The weight factor influences are important for establishing control factors for processes, an optimal complex for factors and engineering tolerances.

References [1]. Bâclea,A., Drugescu,E., Cazacu,N., Dobrovici,S., Some aspects concerning the nitriding steels in fluidized bed, Proceedings of The International Conference on Advanced Processing Technologies AMPT’01, vol. I, pag.131. [2]. Dale H., Besterfield D., Control de Calidad, Prientice Hall Hispano-America, 1995; [3]. Peace G.S., Taguchi methods. A hands-on approach, Adison Wesley Publishing Company, Inc.1993; [4]. Phadke S.M., Quality engineering using robust design, Prentice Hall, Englewood Cliffs, New Jersey, 1989; [5]. Taguchi G., Introduction to quality engineering into products and processes, Asian productivity organization, 1986; [6]. Kunii,D., Levenspiel,O, Fluidization engineering, John Wiley & Sons, Inc., New york, 1969.

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QU7 Optimization of Nitriding Experiments Using Taguchi Methodologies From QE

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