Intelligent Modeling and Optimization of ECM Process Parameters

被引：2

作者：

Jegan, T. M. Chenthil ^{[1
]}

Ravindran, D. ^{[2
]}

Anand, M. Dev ^{[3
]}

机构：

[1] St Xaviers Catholic Coll Engn, Dept Mech Engn, Kanyakumari, India

[2] Natl Engn Coll, Dept Mech Engn, Thoothukudi, India

[3] Noorul Islam Ctr Higher Educ, Dept Mech Engn, Kanyakumari, India

来源：

ARTIFICIAL INTELLIGENCE AND EVOLUTIONARY ALGORITHMS IN ENGINEERING SYSTEMS, VOL 1 | 2015年 / 324卷

关键词：

Electrochemical machining; Artificial neural network; Weighted sum particle swarm optimization; PARTICLE SWARM OPTIMIZATION; MULTIOBJECTIVE OPTIMIZATION;

D O I：

10.1007/978-81-322-2126-5_58

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Electrochemical machining (ECM) is an unconventional process used for the machining of hard materials and metal matrix composites. In the present work, the artificial neural network trained with back-propagation algorithm is used for correlating the interactive and high-order influences of various machining parameters on the predominant machining factors. The operators' requirements cannot be satisfied by the machining parameters provided by ECM machine tool builders. The process parameters are then optimized using weighted sum particle swarm optimization. The fitness function for optimization is obtained from the developed model.

引用

页码：533 / 541

页数：9

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