Grinding process parameter optimization to enhance surface finish using NSGA-II algorithm: an integrated experimental and evolutionary approach

Grinding is a critical machining process used to achieve superior dimensional accuracy and surface integrity in precision components across the automotive, aerospace, and manufacturing industries. This study introduces a hybrid framework that integrates Taguchi-based empirical modeling with Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimization to assess and improve grinding performance while considering dressing parameters within the limitations of practical experimental settings. The parameters studied were dresser depth of cut, dresser cross-feed rate, and grinding feed rate. Experiments were designed using a Taguchi L9 orthogonal array with two replicates per run, yielding 18 trials in total. Surface roughness, grinding power, and grinding ratio were selected as performance objectives. By focusing on the simultaneous optimization of surface roughness, power consumption, and grinding ratio, this framework distinguishes itself by including dressing effects in a multi-objective evolutionary model. The hybrid framework successfully generated Pareto-optimal solutions for conflicting objectives. The best result achieved a surface roughness of 0.2795 μm, with 1.696 kW grinding power and a grinding ratio of 11.1621. The use of a resource-efficient experimental design further enhances its practical applicability, offering sustainable and effective solutions for industrial-grinding processes.