Multivariate Data Analysis of Gas-Metal Arc Welding Process

被引：3

作者：

Ranjan, Rajesh ^{[1
]}

Talati, Anurag ^{[2
]}

Ho, Megan ^{[3
]}

Bharmal, Hussain ^{[4
]}

Bavdekar, Vinay A. ^{[2
]}

Prasad, Vinay ^{[2
]}

Mendez, Patricio ^{[2
]}

机构：

[1] Indian Inst Technol, Dept Polymer & Proc Engn, Roorkee 247667, Uttarakhand, India

[2] Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 2V4, Canada

[3] Univ British Columbia, Dept Mat Engn, Vancouver, BC V6T 1Z3, Canada

[4] Indian Inst Technol, Dept Mech Engn, Bombay 400076, Maharashtra, India

来源：

IFAC PAPERSONLINE | 2015年 / 48卷 / 08期

关键词：

welding; data analysis; batch process; clusteaing; classification; multi-way principal component analysis; partial least squares; hierarchical culstering; Solt sensors;

D O I：

10.1016/j.ifacol.2015.09.011

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Gas-metal arc welding is a Widely used welding,process. The testing of such welds is clone offline and in most cases after the welding operation is over. To monitor the progress of the welding run, it is essential to develop multivariate data analysis techniques that can classify the welds into good or bad runs and also be able to predict the quality variables. In this work, popular multivariate data analysis methods such as hierarchical clustering analysis, principal component analysis and partial least squares are used to develop classification and regression models to predict the weld quality based on various parameters. The results indicate that models obtained using these methods are effective in classification and prediction of weld quality and can be further developed for online and industrial uses in weld run monitoring. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：463 / 468

页数：6

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