Analysis of Multi-Objective Optimization of Machining Allowance Distribution and Parameters for Energy Saving Strategy

Keyan He, Huajie Hong, Renzhong Tang and Junyu Wei
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Keyan He: School of Intelligence and Technology, National University of Defense Technology, Changsha 410073, China
Huajie Hong: School of Intelligence and Technology, National University of Defense Technology, Changsha 410073, China
Renzhong Tang: Industrial Engineering Center, Zhejiang Province Key Laboratory of Advanced Manufacturing Technology, Zhejiang University, Hangzhou 310058, China
Junyu Wei: School of Intelligence and Technology, National University of Defense Technology, Changsha 410073, China

Sustainability, 2020, vol. 12, issue 2, 1-32

Abstract: Machining allowance distribution and related parameter optimization of machining processes have been well-discussed. However, for energy saving purposes, the optimization priorities of different machining phases should be different. There are often significant incoherencies between the existing research and real applications. This paper presents an improved method to optimize machining allowance distribution and parameters comprehensively, considering energy-saving strategy and other multi-objectives of different phases. The empirical parametric models of different machining phases were established, with the allowance distribution problem properly addressed. Based on previous analysis work of algorithm performance, non-dominated sorting genetic algorithm II and multi-objective evolutionary algorithm based on decomposition were chosen to obtain Pareto solutions. Algorithm performances were compared based on the efficiency of finding the Pareto fronts. Two case studies of a cylindrical turning and a face milling were carried out. Results demonstrate that the proposed method is effective in trading-off and finding precise application scopes of machining allowances and parameters used in real production. Cutting tool life and surface roughness can be greatly improved for turning. Energy consumption of rough milling can be greatly reduced to around 20% of traditional methods. The optimum algorithm of each case is also recognized. The proposed method can be easily extended to other machining scenarios and can be used as guidance of process planning for meeting various engineering demands.

Keywords: Pareto front; machining allowance distribution; cutting parameters optimization; energy conservation; economic objectives (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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