Parameters optimization considering the trade-off between cutting power and MRR based on Linear Decreasing Particle Swarm Algorithm in milling

被引:40
|
作者
Han, Fujun [1 ]
Li, Li [1 ]
Cai, Wei [1 ]
Li, Congbo [2 ]
Deng, Xingguo [3 ]
Sutherland, John W. [4 ]
机构
[1] Southwest Univ, Coll Engn & Technol, Chongqing 400715, Peoples R China
[2] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China
[3] Chongqing Three Gorges Univ, Coll Mech Engn, Chongqing 404130, Peoples R China
[4] Purdue Univ, Environm & Ecol Engn, W Lafayette, IN 47906 USA
基金
中国国家自然科学基金;
关键词
Parameters optimization; Energy efficiency; Grey correlation analysis; Linear decreasing particle swarm algorithm; Energy consumption; ENERGY-CONSUMPTION; MATERIAL REMOVAL; EFFICIENCY; TOOL; CARBON; MINIMIZATION; SELECTION; MODEL;
D O I
10.1016/j.jclepro.2020.121388
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
As mechanical engineering is growing a current and urgent issue is rising in the manufacturing process, which is the ability to improve efficiency while reducing energy consumption during the processes. Cutting parameters are an important part of the computer numerical control (CNC) machining process, so a reasonable selection of cutting parameters can significantly enhance the machine's energy efficiency. Previous studies mainly focused on cutting power (P) and material removal rate (MRR), respectively, without considering the trade-off relationship between them; In this paper, an optimization model of cutting parameters is developed by establishing a multi-objective model where P and MRR are identified. The proposed model utilizes the Grey Correlation Analysis (GRA) and experimental to determine the weight of the objective. After the Linear Decreasing Particle Swarm (LDPS) optimization algorithm was utilized to solve the model, several application cases are given and their results demonstrate the ability of our method through comparing with the traditional approach. (C) 2020 Elsevier Ltd. All rights reserved.
引用
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页数:10
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