Prediction of machining performance using RSM and ANN models in hard turning of martensitic stainless steel AISI 420

Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science - Tập 233 Số 13 - Trang 4439-4462 - 2019
Abderrahmen Zerti1, Mohamed Athmane Yallese1, Oussama Zerti1, Mourad Nouioua1, Riad Khettabi1
1Mechanics and Structures Research Laboratory (LMS), Mechanical Engineering Department, Université 8 Mai 1945, Guelma, Algeria

Tóm tắt

The purpose of this experimental work is to study the impact of the machining parameters ( Vc, ap, and f) on the surface roughness criteria ( Ra, Rz, and Rt) as well as on the cutting force components ( Fx, Fy, and Fz), during dry turning of martensitic stainless steel (AISI 420) treated at 59 hardness Rockwell cone. The machining tests were carried out using the coated mixed ceramic cutting-insert (CC6050) according to the Taguchi design (L25). Analysis of the variance (ANOVA) as well as Pareto graphs made it possible to quantify the contributions of ( Vc, ap, and f) on the output parameters. The response surface methodology and the artificial neural networks approach were used for output modeling. Finally, the optimization of the machining parameters was performed using desirability function (DF) minimizing the surface roughness and the cutting forces simultaneously. The results indicated that the roughness is strongly affected by the feed rate ( f) with contributions of (80.71%, 80.26%, and 81.80%) for ( Ra, Rz, and Rt) respectively, and that the depth of cut ( ap) is the factor having the major influence on the cutting forces ( Fx = 53.76%, Fy = 50.79%, and Fz = 65.31%). Furthermore, artificial neural network and response surface methodology models correlate very well with experimental data. However, artificial neural network models show better accuracy. The optimum machining setting for multi-objective optimization is Vc = 80 m/min, f = 0.08 mm/rev and ap = 0.141 mm.

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Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science

Tập 233 Số 13

4439-4462

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