Analysing surface quality evolution and cutting forces in micro-milling of Ti6Al4V using experimental and machine learning approaches

In modern manufacturing, machine learning (ML) plays a crucial role in predicting response measures, reducing machining trial costs and saving energy. This study investigates surface evolution and cutting forces (Favg) during the micro-milling of selective laser-melted (SLM) Ti6Al4V, focusing on spindle speed (SS), depth of cut (DOC), and feed rate (FZ). Analysis of variance (ANOVA) identified FZ as the most influential on surface roughness (Ra) of about 38.54% and DOC on Favg (42.16%). An artificial neural network (ANN) was trained to map the process-performance relationship, showing strong correlation with experimental results, thereby reducing material and resource costs. Additionally, a ML-based non-dominated sorting genetic algorithm (NSGA-II) was used for multi-objective optimization, achieving significant improvements in Ra and Favg by 335.61% and 592.61%, respectively, with optimal parameters of SS = 74,994 RPM, DOC = 19.94 μm, and FZ = 1 μm/tooth. Detailed analysis of Favg signals, tool wear, and surface evolution was conducted using scanning electron microscopy (SEM). The cumulative regression coefficient (R) for the ANN model is 0.99669, which demonstrates a strong correlation between the predicted values and the actual outcomes. The developed ML model will help the manufacturing industry by reducing the costs for materials, resources, and trial runs.