Parameters Optimization of Multipass Milling Process by an Effective Modified Particle Swarm Optimization Algorithm

The selection of the optimal metal milling parameters greatly impacts final product quality and production efficiency in modern manufacturing systems. The profit rate is also sensitive to the selected parameters. This research focuses on determining the optimal parameters of a multipass milling process using an improved particle swarm optimization (PSO) method. The objective is to minimize the production time. The proper number of passes, the optimal cut speed, and feed rate are considered as the parameters (the decision variables in the model) which are needed to be optimized. Furthermore, the permissive arbor strength, arbor deflection, and motor power are the constraints of the model. The penalty function method is used as the constraints handling technique to address the constraints efficiently in the proposed method. A case is adopted and solved to evaluate the performance of the proposed method. The experimental part is analyzed and compared with advanced methods. Experimental results show that the proposed method is very effective for parameters optimization of a multipass milling process and outperforms other methods.