Open AccessCutting parameter optimization method in multi-pass milling based on improved adaptive PSO and SA
Author(s) -
Yilin Fang,
Lili Zhao,
Ping Lou,
Junwei Yan
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1848/1/012116
Subject(s) - particle swarm optimization , mathematical optimization , simulated annealing , multi swarm optimization , energy consumption , production (economics) , computer science , process (computing) , process engineering , engineering , mathematics , electrical engineering , economics , macroeconomics , operating system
In the production process, cutting parameters greatly affect the production cost and energy consumption, so it is very important for manufacturers to optimize cutting parameters. In this paper, an improved particle swarm optimization (PSO) is presented to optimize cutting parameters for minimizing carbon emissions, production cost and processing time in multi-pass milling. First, a multi-objective optimization model of cutting parameters is established with number of milling passes as one of decision variables. Then, an improved adaptive simulated annealing particle swarm optimization (IAPSOSA) is proposed to obtain the optimal solution of cutting parameters. At last, a case study is given to illustrate that the proposed method is effective to optimize cutting parameters for economic and environmental benefits.