A Generic Multi-Objective Optimization of Machining Processes Using an End-to-End Evolutionary Algorithm

Abstract

Machining processes have been widely employed in the modern manufacturing industry to transform raw materials into final products, and they are of great importance in improving the environmental impact and production efficiency of this industry. The selection of appropriate machining process parameters can effectively improve the environmental impact and production efficiency of a process. However, most existing studies on the optimization of these parameters have targeted optimization techniques or modeling methods, and have seldom taken into consideration the adaptability of the machining process. Thus, they suffer from poor generalization and flexibility in actual deployment. Based on this, a generic optimization framework based on the end-to-end evolutionary algorithm was proposed in this study, which can be adapted to various machining optimization problems, to guide the operators in selecting the best parameters in an automated way. Firstly, a modeling framework was introduced to guide the operators to develop optimization objectives. Subsequently, a flexible optimization algorithm was employed to generate Pareto front solutions. Finally, the CRITIC-TOPSIS method was employed to provide a final solution from the different Pareto solutions generated. Experiments were conducted on a milling machine to demonstrate the effectiveness and advantages of the proposed method. The results showed that the proposed method is flexible and applicable for the optimization of the different machining steps and objectives.

Keywords

• machining process
• multi-objective optimization
• end-to-end evolutionary algorithm
• flexible framework
• CRITIC-TOPSIS