Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes

被引：174

作者：

Sardiñas, RQ ^{[1
]}

Santana, MR ^{[1
]}

Brindis, EA ^{[1
]}

机构：

[1] Univ Matanzas, Dept Mech Engn, Matanzas 44740, Cuba

来源：

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2006年 / 19卷 / 02期

关键词：

turning processes; multi-objective optimization; genetic algorithms;

D O I：

10.1016/j.engappai.2005.06.007

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Determination of optimal cutting parameters is one of the most important elements in any process planning of metal parts. This paper presents a multi-objective optimization technique, based on genetic algorithms, to optimize the cutting parameters in turning processes: cutting depth, feed and speed. Two conflicting objectives, tool life and operation time, are simultaneously optimized. The proposed model uses a microgenetic algorithm in order to obtain the non-dominated points and build the Pareto front graph. An application sample is developed and its results are analysed for several different production conditions. This paper also remarks the advantages of multi-objective optimization approach over the single-objective one. (c) 2005 Elsevier Ltd. All rights reserved.

引用

页码：127 / 133

页数：7

共 20 条

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