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ScienceDirect Materials Today: Proceedings 18 (2019) 4589–4597

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ICMPC-2019

Evaluation and optimization of material properties of ASS 316L at elevated temperatures using Response Surface Methodology Ahsan Ul Haqa*, Ajay Kumar Kavita, Thirumal Raob, Tanya Buddib, Dharavath Balojic, K. Satyanarayanab, S.K Singhb, a

Department of Mechanical Engineering, VNR VJIET, Bachupally, Hyderabad. 500090. b Department of Mechanical Engineering, GRIET, Bachupally, Hyderabad. 500090. c Department of Mechanical Engineering, JNTUH, Hyderabad. 500090.

Abstract Austenitic Stainless Steels are in great demand and its application has importance in defense, automotive as well as in nuclear industries. Understanding its properties and deformation behavior becomes the key prerequisite to optimize the process parameters for improving the working condition suitable for processing and ensuring safe performance during hot working. The present work focuses on optimizing the mechanical properties such as ultimate tensile strength, yield strength, and % elongation at different temperatures ranging from 7500C to 9000C at an interval of 750C at two different strain rates (0.1 & 0.001s-1) in three different orientations (00, 450, 900) to the rolling direction. The L18 based design of experiments was used to perform the experiments. The experimental results showed that temperature has a significant effect on material properties. With the increase in temperature, % elongation increases and a considerable decrease in strength was observed. The Ultimate strength, yield strength, and % elongation were optimized using response surface methodology. The temperature found to be the most influencing factor by having a percentage contribution of 64.4% from so obtained ANOVA results. © 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the 9th International Conference of Materials Processing and Characterization, ICMPC-2019 Keywords: ASS316L, hot working, optimization, RSM.

* Corresponding author. Tel.:+91-9642666556 E-mail address: [email protected]

2214-7853© 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the 9th International Conference of Materials Processing and Characterization, ICMPC-2019

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Nomenclature F R MSE DF UTS YTS RSM N K

Fisher’s ratio Coefficient of determination Mean square error Degree of freedom Ultimate tensile strength Yield tensile strength Response surface methodology Strain hardening exponent Strength coefficient

1. Introduction Austenitic stainless steels 316L is a kind of iron-chromium-nickel steel [1] which is being extensively used in modern defense, automotive as well as in nuclear industries [2]. Because of their high chromium and nickel content they are most corrosion resistant steel in the stainless steel group which can even withstand boiling sea water[3]. Primarily, ASS 316L has an austenitic phase and small quantities of ferritic may be present[4] which makes it structure tough and ductile due to which they don’t lose their quality at elevated temperatures because of the arrangement of ferritic iron base alloys[5]. These alloys contain 2-3% of molybdenum which is having enhanced corrosion resistance, whereas resistance to pitting corrosion is due to an increased percentage of chromium which will prevent the material from getting corroded by forming an oxide layer on the surface of the material. Moreover, ASS316L contains low carbon in its chemical composition which will decline the formation of carbides. Since these materials are useful for nuclear reactors as a radiation containment [6], and also used as manufacturing material in nuclear fuel cladding tubes and fuel subassembly wrappers in fast breeder reactors owing to its superior properties at elevated temperatures [7] it becomes commanding to study, analyze and optimize the mechanical properties of the material at elevated temperatures. The amount of volume fraction of austenite and ferrite present in it plays a significant role in deciding the overall mechanical properties of the material and also the fineness of the phases i.e. finer the grains, better will be the mechanical properties. The tensile deformation behavior can be studied by performing various types of tests such as uniaxial tensile, biaxial tensile and compression test [8]. In Recent years, researchers directed a great deal of attention on evaluating the mechanical properties of the material viz... UTS, YS, % elongation, n and k values of ASS 316 and other steel and titanium-based alloys [9 - 11] based on the experimental data obtained from the uniaxial tensile test, and to predict these properties and formability at unknown temperature and strain rate ANN models have been developed [12-15]. Tajveer et al. [16] studied the effect of constrained groove pressing (CGP) on material properties which induces a large amount of plastic strain in the sheet metal. Having knowledge of these properties is very important as they control various production techniques such as bending, deep drawing, stretching operations, etc...[17]. Similarly, efforts were also made to investigate the dynamic strain aging regime in ASS 316 in the temperature range up to 6500C [18]. Singh et al. [19] predicted the material properties of EDD steel in a blue brittle region using ANN. Understanding the material properties and tensile deformation behavior becomes a key prerequisite to optimize the process parameters for improving the working conditions suitable for processing and ensuring safe performance during hot working. In order to enhance the production efficiency while maintaining the quality and to achieve the commercial success of the component, the optimization of the mechanical properties is the key factor to be considered. Process modeling and optimization are the two important issues in production. Whereas, the lack of any model to optimize the mechanical properties of the materials, this article attempts to deliver a methodology. Design of experimental

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methods such as Taguchi methods, factorial design, and response surface methodology is widely being used to reduce the time and cost for manufacturing [20]. Sana Abid et al. [21] proposed a model to describe the evolution of mechanical properties and suggested the empirical models for ultimate strength, yield strength, and deformation which have been validated by numerical and experimental results. Kwangjae and Dennnis [22] presented an alternative modeling approach to optimize the multi-response system, based on maximizing the exponential desirability functions and the proposed approach can be done using any general algorithm for a non-linear problem. Further, optimization of design parameters of modified die used in the hydro-mechanical deep drawing has been carried out using LS DYNA so as to increase the LDR [23]. Therefore, it is important for the researchers to model, quantify and optimize the relationship among various parameters affecting its responses. Optimization of the process parameters not only increases the utility of the technologist but also the quality of the product. Since the production of sheet metal components involves a number of processes such as deep drawing, bending, etc…, the response surface methodology (RSM) which is a collection of mathematical and statistical techniques appears to be a practical and simple method for developing, improving and optimizing the processes over a feasible domain of parameter settings. The RSM is considered one among the emerging and most popular optimization techniques used in recent years [24-25]. From the literature, it is conveyed that warm tensile deformation behavior of ASS 316L alloys is considerably affected by various parameters such as temperature, strain rate, and orientation. Similarly, there has not been any study on the evaluation and optimization of ASS 316L at elevated temperatures which forms the basis for the present investigation. The present investigation aims at evaluating and optimizing the mechanical properties of ASS 316L at elevated temperatures using RSM. 2. Response surface methodology (RSM) A common problem in the process or product design is the selection of optimal parameter levels, involving the simultaneous consideration of multiple response variables called a multi-response problem which is quite prevalent across various application areas. One of the methodologies for optimization is RSM which is a combination of mathematical and statistical techniques and is useful for modeling and analyzing the problem where the desired response is influenced by several variables and the objective is to optimize this response. Frankly speaking, the objective could be either to minimize or maximize the response depending on the parameters designed. In addition, it also quantifies the relationship between the controllable input parameters and obtained desired responses as represented in the following equation: y = φ (temp, orientation, strain rate) Where y is the desired response and φ is the response function. In the analysis, the approximation of y is proposed using the two-factor interaction (2FI) model. The 2FI model of y can be written as follows: y = β0 + ∑

+∑

Where β0is constant, and βi, βij the coefficient of linear and cross product terms respectively. 3. Experimental Study 3.1 Work Material and chemical composition The work material used in this investigation is ASS 316L because of their significant role in nuclear industries. The 0.6mm thick ASS 316L cold rolled sheet is used to conduct the tensile tests. The chemical composition of the as-received material is shown in Table 1. They were cut along three directions viz… 00, 450, 900 to the rolling direction as appeared in the figure 1.

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Table 1. Chemical composition of investigated ASS316L (wt. %) Sample

C

Si

Mn

P

S

Ni

Cr

ASS 316L

0.015

0.19

1.4

0.034

0.005

10.27

16.4

Fig 1 Tensile test specimen

Mo 2.08

Fig 2 Sample with orientation

3.2 Experimental setup The objective of conducting this experiment is to optimize the responses such as ultimate tensile strength, yield strength and % elongation. For this, isothermal uniaxial tensile test was conducted on computer controlled ultimate tensile testing machine (UTM) as shown in figure 2, having a capacity of 50KN and heating capacity of two zone split furnace is from room temperature to 9000C with ±50C accuracy. Samples were heated up to deformation temperature and then maintained for 10 minutes so as to stabilize the temperature uniformly throughout the furnace. High temperature contact type extensometer was used to record the load-displacement data. A feedback control system is incorporated in the UTM to execute the exponential increase of the actuator to obtain the constant true strain rates. The heating elements are made up of Kanthal, capable of resisting high surface load inside the furnace.

Figure 2 Computer controlled UTM with high temperature two zone split furnace 3.3 Experimental Design The aim of experimental design is to minimize the number of experiments while maximizing the quality of the response. The 0.6mm thin sheet specimens were machined as per ASTM E8M standard dimensions using wire cut EDM to obtain the highly finished surface with least distortion [26] in three different orientations as shown in figure 1. a & b. Experiments were performed as per test conditions specified by full factorial experimental design procedure with L18 orthogonal array (OA). A common RSM design, also known as Box-behnken design was

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employed consisting of 18 runs. The experimental sequence was randomized so as to minimize the effects of parameters. The experiments were performed from 7500C to 9000C at an interval of 750C at two different strain rates i.e. 0.1&0.001s-1 to extract the experimental data such as ultimate tensile strength, yield strength, and percentage elongation. 4. Results and discussions 4.1 Hot deformation behavior The material properties are evaluated at two different quasi-static strain rate conditions at three different temperatures in three different orientations. The representative true stress vs. true strain curve at six different settings i.e. a variation of temperature at a particular constant strain rate (figure 3 a, b &c) and variation of orientation at a constant rate (figure 3 d, e &f) are shown. Singh et al. [27] showed the typical flow stress curves indicating that flow stresses are closely related to deformation temperature which are obtained from the isothermal and non-isothermal compressive test. It is noticed from the figures that, temperature plays a vital role in determining the strength of this material as flow stress is significantly influenced by the variation of temperatures. Flow stresses are sharply getting decreased with the rise in temperature which could be because of increasing in stacking fault energy (SFE) which makes the material more flowable at elevated temperatures. Change of orientation at a particular temperature and strain rate did not demonstrate much distinction. However, the percentage elongation is marginally lower in the rolling direction (0°) when compared to other two directions i.e. diagonal (45°) & transverse direction (90°), whereas a higher percentage of elongation is observed at slower strain rate as expected. The distinct material properties are cited in table 2 and as shown in below Fig 4 (a-c). Table 2. Experimental data obtained from the designed matrix at various parameters of the base material. S. No

Temperature

Orientation

UTS

1

750

0

323.06

166.21

37.32

2

750

45

307.804

135.176

39.12

3

750

90

322.415

140.199

34.73

4

750

0

240.49

151.23

51.51

5

750

45

238.976

151.72

61.53

6

750

90

240.687

137.5

56.683

7

825

0

255.515

121.568

42.523

8

825

45

244.13

134.033

52.33

9

825

90

249.788

129.733

53.123

10

825

0

169.633

144.904

59.883

11

825

45

160.661

131.227

76.89

12

825

90

164.499

130.774

76.889

13

900

0

184.59

117.88

63.879

14

900

45

182.511

136.895

68.758

15

900

90

185.64

117.494

64.859

16

900

0

99.407

83.497

82.96

17

900

45

91.346

88.429

97.112

18

900

90

86.491

74.362

91.288

YTS

% elongation

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Figure 3 True stress-strain curves at various temperatures at constant strain rate in 00,450& 900 represented by a, b &c respectively

Figure 4 True stress-strain curves in different orientations at constant temperatures at 7500, 8250 & 9000C represented by a, b &c respectively

4.2 ANOVA analysis In the ANOVA analysis, the test for significance in individual model coefficients was analyzed for UTS, YS and % elongation and corresponding R-squared and Adj R-squared were noted. Table 3,4,5 shows Analysis of Variance table for testing the adequacy of the UTS, YTS and % elongation respectively. Table 3 Analysis of Variance table for testing the adequacy of the UTS Source

Sum of squares

df

Mean of squares

F value

P-value (prob˃F)

Model A-temperature B-strain rate C- orientation AB Residual Cor. Total Standard deviation Mean C.V % press

91990.67 59289.80 32365.28 186.18 149.62 159.96 92150.83 4.00

7 2 1 2 2 10 17

13141.55 29644.90 32365.28 93.09 74.81 16.00

821.55 1853.27 2023.34 5.82 4.68

˂0.0001 ˂0.0001 ˂0.0001 0.0211 0.0368

208.20 1.92 518.27

Ultimate tensile strength = 208+70.70A-42.40B+3.91C+3.55A*B

% contribution significant 64.45 35.18 0.20 0.16

R-squared

0.9983

Adj R-squared Pred R-squared Adeq. precision

0.9970 0.9944 87.469

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Table 4 Analysis of Variance table for testing the adequacy of the YTS Source

Sum of squares

df

Mean of squares

F value

P-value

Model

8703.49

5

1740.70

16.81

˂0.0001

A- temperature

5980.52

2

2990.26

28.88

˂0.0001

B-strain rate

618.87

1

618.87

5.98

0.039

AB

2104.09

2

1052.05

10.16

0.0026

Residual

1242.37

12

103.53

% contribution

(prob˃F)

significant 68.71 7.11 24.18

Cor. Total

9945.86

17

Standard deviation

10.17

R-squared

0.8751

Mean

127.38

Adj R-squared

0.8230

C.V %

7.99

Pred R-squared

0.7189

press

2795.33

Adeq. precision

11.082

Yield tensile strength = 127.38+19.63*A-5.86*B+9.46A*B Table 5 Analysis of Variance table for testing the adequacy of the % Elongation Source

Sum of squares

df

Model

5697.98

13

A-temperature

2955.79

2

B-strain rate

2247.02

1

C- orientation

297.88

2

AB

20.22

AC

109.76

BC Residual Cor. Total Standard deviation

P-value

(prob˃F)

438.31

787.46

˂0.0001

1477.90

2655.19

˂0.0001

37.462

2247.02

4037.01

˂0.0001

56.958

148.94

267.59

˂0.0001

3.775

2

10.11

18.17

0.0098

0.256

4

27.44

49.30

0.0012

0.696

67.30

2

33.65

60.45

0.0010

0.853

2.23

4

0.56

5700.21

17

0.75

F value

R-squared

% contribution significant

0.9996

Mean

61.91

Adj R-squared

0.9983

C.V %

1.21

Pred R-squared

0.9921

press

45.09

Adeq. precision

94.279

% Elongation = 61.91-15.10*A+11.17*B+4.05*C-1.41*A*B-4.01*A*C+1.38*B*C The adequacy of the developed model for UTS, YTS and % elongation has been tested using the statistical analysis of variance (ANOVA) technique. It is noticed that regression is significant with a 95% confidence level as its P value is less than 0.05. The model indicates that the temperature and strain rate and its interaction (A×B) have a significant effect on the material strength. The R2 value is 0.9983, 0.8751 &0.9996 for UTS, YTS & % elongation respectively. It is noteworthy to mention that ‘Pred R2’ values are in good agreement with ‘Adj R2’values and is nearer to 1 that is desirable. From the ANOVA results of % Contribution indicates that temperature is the most influencing factor by having the highest percentage contribution of 64.4% and 68.71% in case of UTS and YTS, Strain rate 56.958% in case of elongation on the material properties.

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5. Conclusions. The current investigation is associated with evaluation and optimization of mechanical properties of 0.6mm thin ASS316L sheet. From the experimental analysis, the following conclusions can be derived. • Variation of temperature significantly influences the flow stress behavior than the orientation variation. The gradual decrease of flow stress is observed with the rise in temperature, and the percentage elongation was marginally low in rolling direction when compared to the other two directions. • Response surface methodology (RSM) is found to be very useful in the process of optimization carried out in the present investigation as the predicted values obtained from models were very close to experimental results. • The temperature found to be the most influencing factor by having a percentage contribution of 64.4% and 68.71% contribution in case of UTS and YTS on material properties from ANOVA results. • Strain rate 56.958% in case of elongation on the material properties from ANOVA results The determination of these relationships remains an open field of research in future especially because of recent advances in materials technology and the available modeling techniques. References [1] Ghosh, N., Pal, P.K., Nandi, G. and Rudrapati, R., 2018. Parametric optimization of gas metal arc welding process by PCA based Taguchi method on austenitic stainless steel AISI 316L. Materials Today: Proceedings, 5(1), pp.1620-1625. [2] Desu, R.K., Krishnamurthy, H.N., Balu, A., Gupta, A.K. and Singh, S.K., 2016. Mechanical properties of Austenitic Stainless Steel 304L and 316L at elevated temperatures. Journal of Materials Research and Technology, 5(1), pp.13-20. [3] Pardo, A., Merino, M.C., Coy, A.E., Viejo, F., Arrabal, R. and Matykina, E., 2008. Pitting corrosion behaviour of austenitic stainless steels–combining effects of Mn and Mo additions. Corrosion Science, 50(6), pp.1796-1806. [4] Mansor, M.S.M., Yusof, F., Ariga, T. and Miyashita, Y., 2018. Microstructure and mechanical properties of micro-resistance spot welding between stainless steel 316L and Ti-6Al-4V. The International Journal of Advanced Manufacturing Technology, pp.1-15. [5] Song, R.B., Xiang, J.Y. and Hou, D.P., 2011. Characteristics of mechanical properties and microstructure for 316L austenitic stainless steel. Journal of iron and steel research, international, 18(11), pp.53-59. [6] Kumar, V. and Arora, H., 2015. Analysis of Sensitization of Austenitic Stainless Steel By Different Welding Processes: A Review. International Journal of Applied Engineering Research, 10(7), pp.1783717848. [7] Karthik, V., Murugan, S., Parameswaran, P., Venkiteswaran, C.N., Gopal, K.A., Muralidharan, N.G., Saroja, S. and Kasiviswanathan, K.V., 2011. Austenitic stainless steels for fast reactors-irradiation experiments, property evaluation and microstructural studies. Energy Procedia, 7, pp.257-263. [8] Nasser, A., Yadav, A., Pathak, P. and Altan, T., 2010. Determination of the flow stress of five AHSS sheet materials (DP 600, DP 780, DP 780-CR, DP 780-HY and TRIP 780) using the uniaxial tensile and the biaxial Viscous Pressure Bulge (VPB) tests. Journal of Materials Processing Technology, 210(3), pp.429-436. [9] Jayahari, L., Sasidhar, P.V., Reddy, P.P., BaluNaik, B., Gupta, A.K. and Singh, S.K., 2014. Formability studies of ASS 304 and evaluation of friction for Al in deep drawing setup at elevated temperatures using LS-DYNA. Journal of King Saud University-Engineering Sciences, 26(1), pp.21-31. [10] Kotkunde, N., Gupta, A.K., Paresi, P.R. and Singh, S.K., 2017. Experimental and Finite Element Studies of Stretch Forming Process for Ti-6Al-4V Alloy at Elevated Temperature. Materials Today: Proceedings, 4(4), pp.5266-5273.

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[11] Singh, S.K. and Gupta, A.K., 2010. Application of support vector regression in predicting thickness strains in hydro-mechanical deep drawing and comparison with ANN and FEM. CIRP Journal of Manufacturing Science and Technology, 3(1), pp.66-72. [12] Kurt, H., Oduncuoglu, M., Yilmaz, N., Ergul, E. and Asmatulu, R., 2018. A Comparative Study on the Effect of Welding Parameters of Austenitic Stainless Steels Using Artificial Neural Network and Taguchi Approaches with ANOVA Analysis. Metals, 8(5), p.326. [13] Tanya Buddi, Swadesh Kumar Singh and B. Nageswara Rao, 2018. Optimum Process Parameters for Plywood Manufacturing using Soya Meal Adhesive. Materials Today: Proceedings (5), pp.18739–18744 [14] Singh, S.K. and Kumar, D.R., 2005. Application of a neural network to predict thickness strains and finite element simulation of hydro-mechanical deep drawing. The International Journal of Advanced Manufacturing Technology, 25(1-2), pp.101-107. [15] Kotkunde, N., Deole, A.D. and Gupta, A.K., 2014. Prediction of Forming Limit Diagram for Ti-6Al-4V Alloy Using Artificial Neural Network. Procedia materials science, 6, pp.341-346. [16] Gupta, A.K., Maddukuri, T.S. and Singh, S.K., 2016. Constrained groove pressing for sheet metal processing. Progress in Materials Science, 84, pp.403-462. [17] Dieter, G.E. and Bacon, D.J., 1986. Mechanical metallurgy(Vol. 3). New York: McGraw-hill. [18] Hussaini, S.M., Singh, S.K. and Gupta, A.K., 2013. Experimental investigation of Dynamic strain aging regime in Austenitic Stainless Steel 316. International Journal of Engineering Research & Technology (IJERT) Vol, 2, pp.1691-1694. [19] Singh, S.K., Mahesh, K. and Gupta, A.K., 2010. Prediction of mechanical properties of extra deep drawn steel in the blue brittle region using Artificial Neural Network. Materials & Design (19802015), 31(5), pp.2288-2295. [20] Kosaraju, S. and Anne, V.G., 2013. Optimal machining conditions for turning Ti-6Al-4V using response surface methodology. Advances in Manufacturing, 1(4), pp.329-339. [21] Abid, S., Messadi, R., Hassine, T., Daly, H.B., Soulestin, J. and Lacrampe, M.F., 2018. Optimization of mechanical properties of printed acrylonitrile butadiene styrene using RSM design. The International Journal of Advanced Manufacturing Technology, pp.1-10. [22] Kim, K.J. and Lin, D.K., 2000. Simultaneous optimization of mechanical properties of steel by maximizing exponential desirability functions. Journal of the Royal Statistical Society: Series C (Applied Statistics), 49(3), pp.311-325. [23] Singh, S.K., Dixit, A. and Kumar, D.R., 2008. Optimization of the design parameters of modified dies in hydro-mechanical deep drawing using LS-DYNA. The International Journal of Advanced Manufacturing Technology, 38(1-2), pp.32-37. [24] Ghasemi, F.A., Ghasemi, I., Menbari, S., Ayaz, M. and Ashori, A., 2016. Optimization of mechanical properties of polypropylene/talc/graphene composites using response surface methodology. Polymer Testing, 53, pp.283-292. [25] Kosaraju, Satyanarayana. and Anne, Venu Gopal., 2013. Optimal machining conditions for turning Ti6Al-4V using response surface methodology. Advances in Manufacturing, 1(4), pp.329-339. [26] Kotkunde, N., Srinivasan, S., Krishna, G., Gupta, A.K. and Singh, S.K., 2016. Influence of material models on theoretical forming limit diagram prediction for Ti–6Al–4V alloy under warm condition. Transactions of Nonferrous Metals Society of China, 26(3), pp.736-746. [27] Lin, Y.C., Wu, F., Wang, Q.W., Chen, D.D. and Singh, S.K., 2018. Microstructural evolution of a NiFe-Cr-base superalloy during non-isothermal two-stage hot deformation. Vacuum, 151, pp.283-293.

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