Wear volume prediction of AISI H13 die steel using response surface methodology and artificial neural network


  • Vishal Jagota Mechanical Engineering Department, National Institute of Technology, Hamirpur, India. Phone: +91 8683038218; Fax: +91 1972-223834
  • Rajesh Kumar Sharma Mechanical Engineering Department, National Institute of Technology, Hamirpur, India




Sliding wear, Response surface methodology (RSM), Artificial neural network (ANN), heat treatment parameters, sensitivity analysis


Resistance to wear of hot die steel is dependent on its mechanical properties governed by the microstructure. The required properties for given application of hot die steel can be obtained with control the microstructure by heat treatment parameters. In the present paper impact of different heat treatment parameters like austenitizing temperature, tempering time, tempering temperature is studied using response surface methodology (RSM) and artificial neural network (ANN) to predict sliding wear of H13 hot die steel. After heat treating samples at austenitizing temperature of 1020°C, 1040°C and 1060°C; tempering temperature 540°C, 560°C and 580°C; tempering time 1hour, 2hours and 3hours, experimentation on pin-on-disc tribo-tester is done to measure the sliding wear of H13 die steel. Box-Behnken design is used to develop a regression model and analysis of variance technique is used to verify the adequacy of developed model in case of RSM. Whereas, multi-layer feed-forward backpropagation architecture with input layer, single hidden layer and an output layer is used in ANN. It was found that ANN proves to be a better tool to predict sliding wear with more accuracy. Correlation coefficient R2 of the artificial neural network model is 0.986 compared to R2 of 0.957 for RSM. However, impact of input parameter interactions can only be analysed using response surface method. In addition, sensitivity analysis is done to determine the heat treatment parameter exerting most influence on the wear resistance of H13 hot die steel and it showed that tempering time has maximum influence on wear volume, followed by tempering temperature and austenitizing temperature. The prediction models will help to estimate the variation in die lifetime by finding the amount of wear that will occur during use of hot die steel, if the heat treatment parameters are varied to achieve different properties.


X. H. Cui, S. Q. Wang, M. X. Wei, and Z. R. Yang, “Wear Characteristics and Mechanisms of H13 Steel with Various Tempered Structures,” J. Mater. Eng. Perform., vol. 20, no. 6, pp. 1055–1062, 2011.

Y. Guanghua et al., “Effects of heat treatment on mechanical properties of h13 steel,” Met. Sci. Heat Treat., vol. 52, no. 7, pp. 393–395, 2010.

A. Bahrami, S. H. M. Anijdan, M. A. Golozar, M. Shamanian, and N. Varahram, “Effects of conventional heat treatment on wear resistance of AISI H13 tool steel,” Wear, vol. 258, no. 5–6, pp. 846–851, 2005.

M. Gojic, L. Kosec, and P. Matkovic, “The effect of tempering temperature on mechanical properties and microstructure of low alloy Cr and CrMo steel,” J. Mater. Sci., vol. 33, pp. 395–403, 1998.

M. Manohar, J. Joseph, T. Selvaraj, and D. Sivakumar, “Application of Box Behnken design to optimize the parameters for turning Inconel 718 using coated carbide tools,” Int. J. Sci. Eng. Res., vol. 4, no. 4, pp. 620–642, Apr. 2013.

N. Senthilkumar, T. Tamizharasan, and S. Gobikannan, “Application of Response Surface Methodology and Firefly Algorithm for Optimizing Multiple Responses in Turning AISI 1045 Steel,” Arab. J. Sci. Eng., vol. 39, pp. 8015–8030, 2014.

B. Wang, J. H. Ma, and Y. P. Wu, “Application of artificial neural network in prediction of abrasion of rubber composites,” Mater. Des., vol. 49, pp. 802–807, 2013.

L. A. Gyurova and K. Friedrich, “Artificial neural networks for predicting sliding friction and wear properties of polyphenylene sulfide composites,” Tribol. Int., vol. 44, pp. 603–609, 2011.

M. X. Wei, S. Q. Wang, L. Wang, X. H. Cui, and K. M. Chen, “Effect of tempering conditions on wear resistance in various wear mechanisms of H13 steel,” Tribol. Int., vol. 44, no. 7–8, pp. 898–905, 2011.

ASM Handbook Volume 4 - Heat Treating. ASM International, 1991.

V. Jagota and R. K. Sharma, “Interpreting H13 steel wear behavior for austenitizing temperature, tempering time and temperature,” J. Brazilian Soc. Mech. Sci. Eng., vol. 40, no. 4, 2018, doi: 10.1007/s40430-018-1140-6.

D. C. Montgomery, Design and Analysis of Experiments, 8th ed. John Wiley and Sons Ink., 2012.

S. Negi, R. K. Sharma, and S. Dhiman, “Experimental Investigation of SLS Process for Flexural Strength Improvement of PA-3200GF Parts,” Mater. Manuf. Process., vol. 30, no. 5, pp. 644–653, 2015.

G. R. Speich and W. C. Leslie, “Tempering of steel,” Metall. Trans., vol. 3, pp. 1043–1054, May 1972.

G. B. Olson, Martensite: a tribute to Morris Cohen. ASM International, 1992.

G. Roberts, G. Krauss, and R. Kennedy, Tool Steels, 5th Edition. ASM Internationa, 1998.

M. R. S. Yazdi, G. S. Bagheri, and M. Tahmasebi, “Finite Volume Analysis and Neural Network Modeling of Wear During Hot Forging of a Steel Splined Hub,” Arab. J. Sci. Eng., vol. 37, pp. 821–829, 2012.

Z. Zhang, N. M. Barkoula, J. K. Kocsis, and K. Friedrich, “Artificial neural network predictions on erosive wear of polymers,” Wear, vol. 255, pp. 708–713, 2003.

S. Taghvaei and R. Vatankhah, “Detection of Unstable Periodic Orbits and Chaos Control in a Passive Biped Model,” Iran. J. Sci. Technol. Trans. Mech. Eng., vol. 40, no. 4, pp. 303–313, 2016.

V. Muthukumar, N. Rajesh, R. Venkatasamy, A. Sureshbabu, and N. Senthilkumar, “Mathematical Modeling for Radial Overcut on Electrical Discharge Machining of Incoloy 800 by Response Surface Methodology,” Procedia Mater. Sci., vol. 6, pp. 1674–1682, 2014.

I. Salehi, M. Shirani, A. Semnani, M. Hassani, and S. Habibollahi, “Comparative Study Between Response Surface Methodology and Artificial Neural Network for Adsorption of Crystal Violet on Magnetic Activated Carbon,” Arab. J. Sci. Eng., vol. 41, pp. 2611–2621, 2016.

J. P. Davim, V. N. Gaitonde, and S. R. Karnik, “Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models,” J. Mater. Process. Technol., vol. 205, pp. 16–23, 2008.

H. Joardar, N. S. Das, G. Sutradhar, and S. Singh, “Application of response surface methodology for determining cutting force model in turning of metal matrix composite,” Measurement, vol. 47, pp. 452–464, 2014.




How to Cite

V. Jagota and R. K. Sharma, “Wear volume prediction of AISI H13 die steel using response surface methodology and artificial neural network”, J. Mech. Eng. Sci., vol. 14, no. 2, pp. 6789–6800, Jun. 2020.