Multi-objective optimization of SUS430C steel turning process using hybrid machine learning and evolutionary algorithm approach - Nguyen Van-Canh; Nguyen Anh-Thang; Pham Ngoc-Linh; Nguyen Thuy-Duong (Results in Engineering)
This study focuses on the turning process of SUS430C stainless steel, a ferritic stainless steel known for its excellent corrosion resistance and moderate mechanical properties, commonly used in automotive and kitchen applications, a material widely used in industrial applications but challenging to machine due to its hardness and work-hardening characteristics. A hybrid approach combining Extreme Gradient Boosting (XGBoost) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was employed to optimize three critical machining objectives: surface roughness (Ra), material removal rate (MRR), and tool wear (Vb). The predictive capability of XGBoost models was validated using experimental data from a Box-Behnken design, achieving high accuracy with R2 values exceeding 0.93 across all performance metrics. These models served as surrogates for fitness evaluation in NSGA-II, enabling efficient multi-objective optimization. The results yielded 16 Pareto-optimal solutions that balance the trade-offs among Ra, MRR, and Vb. Notably, the study highlights the importance of feed rate (fz) and depth of cut (ap) in influencing Ra and Vb, while cutting speed (Vc) significantly impacts MRR. The optimization framework provided practical insights into machining parameter selection, with the lowest Ra of 0.85 µm achieved at Vc=183.01 m/min, fz=0.08 mm/rev, and ap=1.22 mm. The findings underscore the effectiveness of the hybrid XGBoost-NSGA-II approach in solving complex manufacturing optimization problems and serve as a foundation for future applications in sustainable and efficient machining practices.