Multi-objective optimization of dry and lubricated drilling of AISI 310 S stainless steel using indexable drills
摘要
The poor machinability of austenitic stainless steels and the environmental impact of conventional cutting fluids represent major challenges in modern manufacturing. This study investigates the drilling performance of AISI 310 S stainless steel — a high-chromium (25 wt%), high-nickel (20 wt%) alloy widely used in high-temperature industrial applications such as furnace components, heat exchangers, and exhaust systems — under dry and lubricated conditions using a 17 mm Sandvik Coromant indexable carbide insert drill. To the best of the authors’ knowledge, no prior study has simultaneously evaluated machining quality indicators (surface roughness, burr height) and energy efficiency metrics (cutting power, specific cutting energy) for the drilling of AISI 310 S stainless steel using indexable carbide insert drills under both dry and lubricated conditions. This constitutes the main novelty of the present work. A full factorial experimental design (3²) combined with analysis of variance (ANOVA) and regression analysis was employed to evaluate the influence of cutting speed (Vc: 120–185 m/min) and feed rate (f: 0.06–0.14 mm/rev) on four responses: cutting power (P), burr height (H), surface roughness (Ra), and specific cutting energy (E). Feed rate was identified as the dominant factor, contributing over 90% of variance in cutting power and surface roughness. Multi-objective optimization via the Derringer–Suich desirability function identified optimal conditions of Vc = 171.9 m/min and f = 0.06 mm/rev under lubrication, achieving P = 5.26 kW, E = 7.50 J/mm³, H = 0.516 mm, and Ra = 1.46 μm, with a global desirability of D = 0.781. Compared with the optimal dry-drilling solution, the optimal lubricated-drilling solution achieved reductions of 23.3% in cutting power, 48.1% in burr height, and 35.1% in surface roughness, while exhibiting a marginal increase of 4.1% in specific cutting energy. All predictive models were validated using ANOVA and cross-validation metrics (R² > 0.95, R²pred > 0.81), and confirmation experiments yielded prediction errors below 5.2%, confirming the reliability of the developed models. The results provide quantitative guidelines for sustainable and efficient drilling of heat-resistant austenitic stainless steels.