Multi-objective particle swarm algorithm for the posterior selection of machining parameters in multi-pass turning

• 發表在: Journal of King Saud University: Engineering Sciences
• 主要作者: Toufik Ameur
• 格式: Article
• 語言: 英语
• 出版: Springer 2021-05-01
• 主題: Multi-pass turning operations; Multi-objective optimization; Pareto methods; Particle swarm algorithm; Cutting conditions
• 在線閱讀: http://www.sciencedirect.com/science/article/pii/S1018363920302415

The approach presented in this paper addresses the machining process optimization problem through realistic modeling of multi-pass operations. It is designed to determine, at the same time, the number of passes and the cutting conditions of each. This is a multi-objective optimization model that simultaneously minimizes the production rate and the used tool life under all technological and organizational constraints based on fundamental cutting laws. The posterior selection of a solution is made from a Pareto front generated by a multi-objective particle swarm algorithm based on the concept of dynamic neighborhood. In an example application which consists in determining the cutting conditions for a turning operation, using this approach has provided a rich set of Pareto optimal solutions that represents all possible compromises. This set offers, normally, all the information needed for the optimal selection of cutting conditions. Despite the complexity of treated problem, the analysis of the obtained results demonstrates the effectiveness of the developed approach. Thus, it presents the possibility of using this approach for other problems from industry.