A Dynamic Multi-Swarm Particle Swarm Optimizer for Multi-Objective Optimization of Machining Operations Considering Efficiency and Energy Consumption

Lijun Song, Jing Shi, Anda Pan, Jie Yang and Jun Xie
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Lijun Song: Department of Industrial Engineering, Chongqing University of Technology, Chongqing 400054, China
Jing Shi: Department of Mechanical and Materials Engineering, University of Cincinnati, Cincinnati, OH 45221, USA
Anda Pan: Department of Industrial Engineering, Chongqing University of Technology, Chongqing 400054, China
Jie Yang: Department of Industrial Engineering, Chongqing University of Technology, Chongqing 400054, China
Jun Xie: Chongqing Key Laboratory of Manufacturing Equipment Mechanism Design and Control, Chongqing Technology and Business University, Chongqing 400067, China

Energies, 2020, vol. 13, issue 10, 1-18

Abstract: Facing energy shortage and severe environmental pollution, manufacturing companies need to urgently energy consumption, make rational use of resources and improve economic benefits. This paper formulates a multi-objective optimization model for lathe turning operations which aims to simultaneously minimize energy consumption, machining cost and cutting time. A dynamic multi-swarm particle swarm optimizer (DMS-PSO) is proposed to solve the formulation. A case study is provided to illustrate the effectiveness of the proposed algorithm. The results show that the DMS-PSO approach can ensure good convergence and diversity of the solution set. Additionally, the optimal machining parameters are identified by fuzzy comprehensive evaluation (FCE) and compared with empirical parameters. It is discovered that the optimal parameters obtained from the proposed algorithm outperform the empirical parameters in all three objectives. The research findings shed new light on energy conservation of machining operations.

Keywords: energy efficiency; machining operation; multi-objective optimization; fuzzy comprehensive evaluation; particle swarm optimizer (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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