Multi-objective optimization of multi-pass face milling using particle swarm intelligence

• Published in: International journal of advanced manufacturing technology Vol. 56; no. 5-8; pp. 429 - 443
• Main Authors: Yang, Wen-an, Guo, Yu, Liao, Wenhe
• Format: Journal Article
• Language: English
• Published: London Springer-Verlag 01.09.2011; Springer Nature B.V
• Subjects: Algorithms; CAE) and Design; Computer-Aided Engineering (CAD; Cutting force; Cutting speed; Engineering; Face milling; Feed rate; Goal programming; Industrial and Production Engineering; Machine tools; Mechanical Engineering; Media Management; Milling (machining); Multiple objective analysis; Original Article; Parameters; Pareto optimization; Pareto optimum; Performance measurement; Production planning; Surface roughness; Swarm intelligence; Multi-object optimization; Face milling; Particle swarm optimization; Machining parameters; Multi-pass
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-011-3187-8

In this paper, to facilitate manufacturing engineers have more control on the machining operations, the optimization issue of machining parameters is handled as a multi-objective optimization problem. The optimization strategy is to simultaneously minimize production time and cost and maximize profit rate meanwhile subject to satisfying the constraints on the machine power, cutting force, machining speed, feed rate, and surface roughness. An efficient fuzzy global and personal best-mechanism-based multi-objective particle swarm optimization (F-MOPSO) to optimize the machining parameters is developed to solve such a multi-objective optimization problem in optimization of multi-pass face milling. The proposed F-MOPSO algorithm is first tested on several benchmark problems taken from the literature and evaluated with standard performance metrics. It is found that the F-MOPSO does not have any difficulty in achieving well-spread Pareto optimal solutions with good convergence to true Pareto optimal front for multi-objective optimization problems. Upon achieving good results for test cases, the algorithm was employed to a case study of multi-pass face milling. Significant improvement is indeed obtained in comparison to the results reported in the literatures. The proposed scheme may be effectively employed to the optimization of machining parameters for multi-pass face milling operations to obtain efficient solutions.