Modeling and multi-objective optimization for minimizing surface roughness, cutting force, and power, and maximizing productivity for tempered stainless steel AISI 420 in turning operations

  • Abderrahmen ZertiEmail author
  • Mohamed Athmane Yallese
  • Ikhlas Meddour
  • Salim Belhadi
  • Abdelkrim Haddad
  • Tarek Mabrouki


The present study aims at investigating the influence of the different machining parameters represented by the cutting speed (Vc), the depth of cut (ap), and the feed rate (f) on the output performance parameters expressed through the surface roughness, the cutting force and power, and the material removal rate (i.e., Ra, Fz, Pc, and MRR) during dry hard turning operation of martensitic stainless steel (AISI 420) treated at 59HRC. The machining tests were carried out using the coated mixed ceramic insert (CC6050) according to the Taguchi design (L25). The analysis of the variance (ANOVA) and the Pareto chart analysis led to quantifying the influence of the (Vc, ap, and f) on the output parameters. The response surface methodology (RSM) and the artificial neural networks (ANN) approaches were applied and compared for output parameters modeling. Attempt was further made to optimize the machining parameters using the desirability function (DF). Four objectives were considered including the maximization of the quality and the productivity (through minimizing Ra and maximizing MRR), and reducing the energy consumption over minimizing both (Fz) and (Pc). The results indicated that (Ra) is strongly influenced by the feed rate (in the order of 80.71%), while the depth of cut seems to be the property having the most influence on the cutting force (65.31%), the cutting power (37.56%), and the material removal rate (36.45%). Furthermore, ANN and RSM models were found to predict well experimental results with the former showing higher accuracy. The machining of AISI 420 (59 HRC) steel with coated ceramic led to achieving a quality surface comparable to that found in grinding (i.e., Ra < 0.4 μm).


Hard turning Modeling Response surface methodology Artificial neural networks Surface roughness Cutting force Martensitic stainless steel Optimization 


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© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Mechanics and Structures Research Laboratory (LMS), Mechanical Engineering DepartmentUniversité 8 Mai 1945 GuelmaGuelmaAlgeria
  2. 2.École Nationale Supérieure de Technologie (ENST)AlgiersAlgeria
  3. 3.Laboratoire de Mécanique Appliquée des Nouveaux Matériaux (LMANM)Université 8 Mai 1945 GuelmaGuelmaAlgeria
  4. 4.ENITUniversity of Tunis El ManarTunisTunisia

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