Multiobjective optimization of end milling parameters for enhanced machining performance on 42CrMo4 using machine learning and NSGA-III

Abstract

The present study analyzes and optimizes machining characteristics, including feed rate (f_z), depth of cut (a_p), cutting speed (V_c), cutter-coated material (M_tc) and cutting-edge radius (r_t), impacting on surface roughness (Ra), material removal rate (MRR) and tool wear (VB) of 42CrMo4 steel during dry end-milling. A total of 108 experimental runs were conducted, focusing on Ra, VB and MRR as response parameters. The nano TiAlN PVD-coated tool yielded better results for Ra and VB than did the TiCN/Al₂O₃ MT-CVD-coated tool. Then, Ra, VB and MRR optimization was carried out simultaneously using a Non-Dominated Sorting Genetic Algorithm III (NSGA-III) and Machine Learning (ML) models. Pareto solutions were found to offer a range of values for the three performance objectives: Ra (0.315–0.556 µm), VB (12.33–32.48 µm) and MRR (0.44–3.58 cm³/min). A quantitative performance score (Ps) ranking index was calculated to rank Pareto solutions for practical case studies. Validation experiments were subsequently performed to affirm that the optimal solution fell within a reasonable error range, with MAPE of 9.58% for Ra, 9.25% for VB and 13.39% for MRR. The validation results underscore the versatility of this approach, suggesting its applicability to a wide array of machining optimization challenges.

Highlights

• 108 experiments were conducted considering cutting speed (V_c), axial depth of cut (a_p), feed per tooth (f_z), cutting-edge radius (r_t) and cutting-coated material (M_tc);

• The effect of cutting characteristics on surface roughness (Ra), flank wear (VB) and material removal rate (MRR) of 42CrMo4 was revealed;

• Machine learning models were developed to predict Ra, VB and MRR accurately;

• The multiobjective NSGA-III technique was employed to optimize Ra, VB and MRR simultaneously;

• These Pareto solutions underwent meticulous evaluation using a quantitative ranking index—the performance score (PS)—which thoughtfully considered user preferences and reliability;

• Experiments were performed to validate the optimum solution within an acceptance range of errors;

Keywords:

• End milling
• 42CrMo4 steel
• machine-learning
• multiobjective optimization

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data is available on request from the authors.