Multisensor-based real-time quality monitoring by means of feature extraction, selection and modeling for Al alloy in arc welding

• 41 feature parameters were extracted from online spectrum, sound and arc voltage signals.
• Singularity of the feature parameters is the result of coupling effect between the simulated defect and the heat accumulation with different degree.
• Optimal, insufficient, complementary and redundancy feature subset space was determined based on developed feature selection approach, i.e., hybrid fisher-based filter and wrapper.
• Multiple welding defects, like porosity, incomplete penetration and so on, have been successfully identified by means of the established classification architecture support vector machine-cross validation (SVM-CV).

Multisensory data fusion-based online welding quality monitoring has gained increasing attention in intelligent welding process. This paper mainly focuses on the automatic detection of typical welding defect for Al alloy in gas tungsten arc welding (GTAW) by means of analzing arc spectrum, sound and voltage signal. Based on the developed algorithms in time and frequency domain, 41 feature parameters were successively extracted from these signals to characterize the welding process and seam quality. Then, the proposed feature selection approach, i.e., hybrid fisher-based filter and wrapper was successfully utilized to evaluate the sensitivity of each feature and reduce the feature dimensions. Finally, the optimal feature subset with 19 features was selected to obtain the highest accuracy, i.e., 94.72% using established classification model. This study provides a guideline for feature extraction, selection and dynamic modeling based on heterogeneous multisensory data to achieve a reliable online defect detection system in arc welding.