Optimization of milling parameters using artificial neural network and artificial immune system

被引：25
|
作者：
Mahdavinejad, Ramezan Ali [1 ,2 ]
Khani, Navid [1 ]
Fakhrabadi, Mir Masoud Seyyed [1 ]
机构：
[1] Univ Tehran, Coll Engn, Sch Mech Engn, Tehran, Iran
[2] Univ Tehran, Fac Engn, Sch Mech Engn, Tehran, Iran
来源：
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY | 2012年 / 26卷 / 12期
关键词：
Milling; Ti-6Al-4V; Artificial neural network; Artificial immune system; FUZZY INFERENCE SYSTEM; SURFACE-ROUGHNESS; PREDICTION;
D O I：
10.1007/s12206-012-0882-9
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
The present paper is an attempt to predict the effective milling parameters on the final surface roughness of the work-piece made of Ti-6Al-4V using a multi-perceptron artificial neural network. The required data were collected during the experiments conducted on the mentioned material. These parameters include cutting speed, feed per tooth and depth of cut. A relatively newly discovered optimization algorithm entitled, artificial immune system is used to find the best cutting conditions resulting in minimum surface roughness. Finally, the process of validation of the optimum condition is presented.
引用
收藏
页码：4097 / 4104
页数：8
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