Review of empirical modelling techniques for modelling of turning process

被引：30

作者：

Garg, Akhil ^{[1
]}

Bhalerao, Yogesh ^{[2
]}

Tai, Kang ^{[1
]}

机构：

[1] Nanyang Technol Univ, Sch Mech & Aerosp Engn, 50 Nanyang Ave, Singapore 639798, Singapore

[2] MIT Acad Engn, Dept Mech Engn, Pune 412105, Maharashtra, India

来源：

INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL | 2013年 / 20卷 / 02期

关键词：

empirical; modelling; turning; artificial neural network; ANN; review; regression; analysis; genetic programming; support vector machines; SVM;

D O I：

10.1504/IJMIC.2013.056184

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The most widely and well known machining process used is turning. The turning process possesses higher complexity and uncertainty and therefore several empirical modelling techniques such as artificial neural networks, regression analysis, fuzzy logic and support vector machines have been used for predicting the performance of the process. This paper reviews the applications of empirical modelling techniques in modelling of turning process and unearths the vital issues related to it.

引用

页码：121 / 129

页数：9

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