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Machining scheme selection based on a new discrete particle swarm optimization and analytic hierarchy process

Published online by Cambridge University Press:  20 January 2014

Yan-Juan Hu ,
Yao Wang ,
Zhan-Li Wang ,
Yi-Qiang Wang  and
Bang-Cheng Zhang
Yan-Juan Hu
Affiliation:
School of Mechatronic Engineering, Changchun University of Technology, Changchun, China
Yao Wang
Affiliation:
College of Mechanical Engineering, Beihua University, Jilin, China
Zhan-Li Wang*
Affiliation:
School of Mechatronic Engineering, Changchun University of Technology, Changchun, China
Yi-Qiang Wang
Affiliation:
Ningbo Institute of Technology, Zhejiang University, Ningbo, China
Bang-Cheng Zhang
Affiliation:
School of Mechatronic Engineering, Changchun University of Technology, Changchun, China
*
Reprint requests to: Zhan-li Wang, School of Mechatronic Engineering, Changchun University of Technology, Changchun, 130012, China. E-mail:wangzl@mail.ccut.edu.cn
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Abstract

The goal of machining scheme selection (MSS) is to select the most appropriate machining scheme for a previously designed part, for which the decision maker must take several aspects into consideration. Because many of these aspects may be conflicting, such as time, cost, quality, profit, resource utilization, and so on, the problem is rendered as a multiobjective one. Consequently, we consider a multiobjective optimization problem of MSS in this study, where production profit and machining quality are to be maximized while production cost and production time must be minimized, simultaneously. This paper presents a new discrete method for particle swarm optimization, which can be widely applied in MSS to find out the set of Pareto-optimal solutions for multiobjective optimization. To deal with multiple objectives and enable the decision maker to make decisions according to different demands on each evaluation index, an analytic hierarchy process is implemented to determine the weight value of evaluation indices. Case study is included to demonstrate the feasibility and robustness of the hybrid algorithm. It is shown from the case study that the multiobjective optimization model can simply, effectively, and objectively select the optimal machining scheme according to the different demands on evaluation indices.

Keywords

Analytic Hierarchy ProcessDiscrete Particle Swarm OptimizationMachining Scheme SelectionMultiobjective Optimization
Type
Regular Articles
Information
AI EDAM , Volume 28 , Issue 1 , February 2014 , pp. 71 - 82
Copyright
Copyright © Cambridge University Press 2014 

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