High-performance turning of WCu alloy using cryogenically treated tool with predictive machine learning approach and Taguchi–Grey relational optimization

≈ 0.5). Surface roughness prediction was poor for all models, which indicates impact of unmeasured dynamic factors. Grey Relational Analysis identified the optimal machining parameters as 1200 rpm cutting speed, 0.04 mm/rev feed rate, and 0.5 mm depth of cut. Grey Relational Analysis integrated multi-response outcomes and revealed that optimal machining parameters for both tool types are 1200 rpm cutting speed, 0.04 mm/rev feed rate, and 0.5 mm depth of cut. Overall, the study confirms that cryogenic treatment substantially enhances machinability of WCu alloy and, when combined with data-driven predictive modelling, provides a robust pathway for optimizing precision turning operations in industrial applications.