Intelligent Optimization of Dry Machining for Machinability Enhancement of Super Duplex Stainless Steel

Abstract

Sustainable manufacturing increasingly demands environmentally friendly machining strategies, and dry machining has become recognized as a sustainable alternative to conventional coolant-assisted processes. This study presents a framework built on a machine learning technique for optimizing the dry machining performance of Super Duplex Stainless Steel (SDSS 2507) using textured cutting inserts. Gaussian process regression (GPR) models were developed to predict maximum roughness depth (Rmax) and maximum flank wear (VBmax). Gaussian data augmentation was employed to enhance model generalization. The predictive performance was strong, with R2 values recorded above 0.95 on testing datasets. To identify optimal machining parameters, GPR was integrated with particle swarm optimization (PSO), enabling independent optimization of Rmax and VBmax. The framework achieved reductions of 13.97% in Rmax and 30.70% in VBmax compared to experimental benchmarks. The results confirm the effectiveness of data-driven optimization in enhancing surface quality, tool performance, and intelligent machining control.