Statistical analysis, modeling and multi-objective optimization of parameters intermittent turning process of AISI D3

Fethi Khelfaoui; M. A. Yallese; N. Ouelaa; S. Chihaoui; S. Belhadi

doi:10.15282/jmes.17.2.2023.8.0752

Authors

F. Khelfaoui Laboratory of Mechanics and Structure (LMS), Department of Mechanical Engineering, University 8 May 1945, BP 401 Guelma 24000, Algeria. Phone: +213 37 10 05 53; Fax: + 213 37 10 05 55
M. A. Yallese Laboratory of Mechanics and Structure (LMS), Department of Mechanical Engineering, University 8 May 1945, BP 401 Guelma 24000, Algeria. Phone: +213 37 10 05 53; Fax: + 213 37 10 05 55
N. Ouelaa Laboratory of Mechanics and Structure (LMS), Department of Mechanical Engineering, University 8 May 1945, BP 401 Guelma 24000, Algeria. Phone: +213 37 10 05 53; Fax: + 213 37 10 05 55
S. Chihaoui Laboratory of Mechanics and Structure (LMS), Department of Mechanical Engineering, University 8 May 1945, BP 401 Guelma 24000, Algeria. Phone: +213 37 10 05 53; Fax: + 213 37 10 05 55
S. Belhadi Laboratory of Mechanics and Structure (LMS), Department of Mechanical Engineering, University 8 May 1945, BP 401 Guelma 24000, Algeria. Phone: +213 37 10 05 53; Fax: + 213 37 10 05 55

DOI:

https://doi.org/10.15282/jmes.17.2.2023.8.0752

Keywords:

Intermittent turning, Machinibility, AISI D3, Power consumption, Modling, Optimization

Abstract

Intermittent machining is characterized by its complex and irregular context. This intermittency causes machining to occur under difficult conditions that greatly influence the technological performance parameters. The aim of the present work is to evaluate the effects of input parameters, cutting speed, V_c, depth of cut, a_p, tool nose radius, r and feed rate, f, on surface roughness, R_a, tangential cutting force, F_z, motor power consumption, P_m, cutting power, P_c and material removal rate (MRR), during intermittent turning (IT) of AISI D3 tool steel. Machining was performed with a triple CVD coated carbide tool (AI₂O₃/TiC/TiCN) by adopting a Taguchi L9 (3^4) experimental design. The ANOVA and RSM methods were used to analyze the effects of cutting factors on the outputs parameters resulting in statistical prediction models. In addition, a multi-objective optimization of the cutting conditions exploiting the desirability function (DF) was done according to four cases of relative importance corresponding to different industrial contexts. Furthermore, the grey relational analysis (GRA) method was applied and compared with the DF method. The results show that the optimal regime found by the DF method, (r =1.6mm, V_c= 240 m/min, f = 0.084 mm/rev and a_p= 0.64 mm), favors R_a and MRR. On the other hand, for the GRA method, the combination of (r = 0.4 mm, V_c= 240 m/min f = 0.08 mm/rev and a_p= 0.3 mm) favors the minimization of F_z, P_m and P_c. This work presents an originality because the results found are very useful in the field of optimization for a better control of the process IT.

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