Optimization techniques for material selection and manufacturing processes: a review

The optimum choice of process variables is critical for assuring product quality, lowering machining costs, and enhancing manufacturing process efficiency. This review paper introduces the application of contemporary optimization techniques to optimize advanced material selection and manufacturing processes to enhance their machining performance parametrically. It focuses on mathematical modeling to achieve these improvements. The discussion includes manufacturing optimization techniques neural network-based methods, genetic algorithms, particle swarm optimizations, teaching–learning-based optimizations, simulated annealing, Ant colony optimizations, fuzzy optimizations, artificial bee colony algorithm, and harmony search. The material selection optimization technique includes The life cycle assessment, multi-objective optimization, artificial neural networks, and genetic algorithms. The main objective of this paper is to comprehensively present the diverse range of modern optimization methods and their applications. It will helpful to the researcher to select the appropriate optimization method in the field of materials selection and manufacturing process to achieve effective output results in the various engineering applications.