Global Machining Prediction and Optimization

Optimization in the manufacturing process enhances the quality and productivity of the industry. This chapter is aimed at discussing and reviewing global optimization and prediction techniques in the machining process. This study covered the review of heuristic and metaheuristic techniques for advanced manufacturing optimization, such as the artificial neural network, adaptive neuro-fuzzy inference system, particle swarm optimization (PSO), genetic algorithm (GA), whale optimization algorithm (WOA), ant lion optimization algorithm (ALOA), and grasshopper optimization algorithm (GOA). The various procedures and steps for implementing these techniques were also covered. Also, a review of related literature on optimization techniques in the machining process was carried out to enable a concrete study to significantly improve the machining process. According to the findings, global optimization techniques are viable, sustainable, and can be used to optimize machining parameters and responses during the manufacturing process. However, several studies have implemented the techniques for only multi-objective optimization; therefore, this study will recommend that studies of multi-response-objective optimization be carried out.