Feature Extraction and Modeling of Welding Quality Monitoring in Pulsed Gas Touch Argon Welding Based on Arc Voltage Signal

Arc sensing plays a significant role in the control and monitoring of welding quality for aluminum alloy pulsed gas touch argon welding （GTAW）. A method for online quality monitoring based on adaptive boosting algorithm is proposed through the analysis of acquired arc voltage signal. Two feature ext...

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Published inShanghai jiao tong da xue xue bao Vol. 19; no. 1; pp. 11 - 16
Main Author 张志芬 钟继勇 陈玉喜 陈善本
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2014
Subjects
Algorithms
Arc welding
Architecture
Classification
Computer Science
Electric potential
Electrical Engineering
Engineering
Feature extraction
Life Sciences
Machine learning
Materials Science
Monitoring
Welding
TP 751.1
Adaboost algorithm
gas touch argon welding (GTAW)
feature extraction
arc voltage signal
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Summary:Arc sensing plays a significant role in the control and monitoring of welding quality for aluminum alloy pulsed gas touch argon welding （GTAW）. A method for online quality monitoring based on adaptive boosting algorithm is proposed through the analysis of acquired arc voltage signal. Two feature extraction algorithms were developed in time domain and frequency domain respectively to extract six statistic characteristic parameters before removing the pulse interference using the wavelet packet transform （WPT）, based on which the Adaboost classification model is successfully established to evaluate and classify the welding quality into two classes and the classified accuracy of the model is as high as 98.81%. The Adaboost algorithm has been verified to be feasible in the online evaluation of welding quality.
Bibliography:ZHANG Zhi-fen, ZHONG Ji-yong , CHEN Yu-xi , CHEN Shan-ben （Institute of Welding Engineering, School of Material Science and Engineering, Shanghai Jiaotong University, Shanghai 200240, China）
31-1943/U
Arc sensing plays a significant role in the control and monitoring of welding quality for aluminum alloy pulsed gas touch argon welding （GTAW）. A method for online quality monitoring based on adaptive boosting algorithm is proposed through the analysis of acquired arc voltage signal. Two feature extraction algorithms were developed in time domain and frequency domain respectively to extract six statistic characteristic parameters before removing the pulse interference using the wavelet packet transform （WPT）, based on which the Adaboost classification model is successfully established to evaluate and classify the welding quality into two classes and the classified accuracy of the model is as high as 98.81%. The Adaboost algorithm has been verified to be feasible in the online evaluation of welding quality.
gas touch argon welding （GTAW）, arc voltage signal, feature extraction, Adaboost algorithm
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1007-1172
1995-8188
DOI:10.1007/s12204-014-1471-0