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.
ZHANG Zhi-fen* (张志芬), ZHONG Ji-yong (钟继勇), CHEN Yu-xi (陈玉喜), CHEN Shan-ben (陈善本)
. Feature Extraction and Modeling of Welding Quality Monitoring in Pulsed Gas Touch Argon Welding Based on Arc Voltage Signal[J]. Journal of Shanghai Jiaotong University(Science), 2014
, 19(1)
: 11
-16
.
DOI: 10.1007/s12204-014-1471-0
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