Advanced Optimization Techniques for CNC Machining: A Comparative Study of Genetic Algorithms, Simulated Annealing, and Particle Swarm Optimization

Abstract

In the manufacturing sector, for improving the CNC machining processes is very important as it helps to increase the efficiency and quality of products. In the prior studies, optimization strategies are emphasized as the most successful way of handling machining problems. yet, the two economies didn't have more elaborate studies done. This study addresses the issue by the comparison of the GA’s effectiveness with two other algorithms: crossover and mutation. Simulated annealing (SA). abord machine learning, neural networks, and particle swarm optimization (PSO) for optimizing CNC machining. The primary limitation of the past studies was the omission of a holistic comparative analysis, based on which our strategy became to implement the separate experiments for each method. Leveraging performance metrics, such as chip removal rate, surface quality (roughness), tool life, energy consumption, and machining cost. Measuring devices were used in different cycles of each method to collect data. Our findings demonstrate significant impacts: In GA the advancement was of 15% in material removal rate and surface roughness was decreased by 10%, and in SA the tool life was extended by 18% and energy consumption was reduced by 10%, finally in PSO material removal rate was improved by 18% and tool life was extended by 22%. The above findings emphasize the need of developing an optimization plan with a clear focus on specific goals and limitations when CNC machining. By means of the study we also enrich the body of knowledge regarding the role of the optimization algorithms in solving the life real machining problems. the study of hybrids methodologies will be conducted so that future study can be carried out including the integration of these techniques with the emerging production technologies for more efficiency and leading to better quality.

Topics & Concepts