Integrated experimental and nature-inspired optimization approach for machining thin-walled AZ31B magnesium alloy
Journal of Magnesium and Alloys, cilt.23, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 23
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.jma.2026.102254
- Dergi Adı: Journal of Magnesium and Alloys
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Anahtar Kelimeler: AZ31B magnesium alloy, Enhanced nature-inspired optimization, Ranque-Hilsch vortex tube, Thin-walled
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
The escalating demand for high specific-strength materials in the aerospace and automotive sectors has spurred significant interest in thin-walled magnesium alloys, whose inherent machining complexities necessitate rigorous parameter optimization to ensure process stability and product integrity. This study develops optimized milling strategies for thin-walled AZ31B magnesium alloy to simultaneously minimize cutting force, cutting temperature, and surface roughness. Thin-walled milling experiments were conducted using three cutting speeds and three feed values under four machining environments: dry, Ranque-Hilsch vortex tube (RHVT), minimum quantity lubrication (MQL), and nanofluid-assisted MQL (N-MQL). The experimental results were optimized using four recent nature-inspired multi-objective optimization models: adaptive distance-based multi-objective particle swarm optimization algorithm with simple position update (ADMOPSO), artificial bee colony variant based on multiple indicators (MIMaOABC), guided population archive whale optimization algorithm (GPAWOA), and modified Boltzmann-based multi-objective Grey Wolf Optimizer (MBB-MOGWO). The comparative performance analysis showed that GPAWOA was the best-performing algorithm overall, achieving the highest Hypervolume and the lowest Spacing values, while MBB-MOGWO exhibited the lowest computational cost. The optimization process generated two distinct Pareto fronts associated with the N-MQL and RHVT environments. By applying a multi-criteria decision-making approach to these Pareto fronts, the optimal cutting conditions were identified as the N-MQL environment with a feed value of 0.12 mm/rev and cutting speeds ranging from 63 to 69 m/min.