Welding penetration recognition based on arc sound and electrical signals in K-TIG welding

Abstract This paper presents a novel methodology for welding penetration recognition in Keyhole Tungsten Inert Gas(K-TIG) welding. A multisensor sensing system is established to acquire the signal of arc sound, arc current, and arc voltage. The Spectral Noise Subtraction(SNS) method is introduced to extract the pure arc sound from the collected sound signal. The interference of electromagnetic field induced by the high current arc is investigated and thus the generation mechanism of arc sound in K-TIG welding is revealed. Some particular features are extracted and the Principal Component Analysis(PCA) is utilized to reduce the dimension of features. Finally, a Support Vector Machine with ten-fold cross validation, grid search optimization, and Error-Correcting Output Codes(ECOC-SVM-GSCV) is utilized to identify partial penetration, full penetration, and excessive penetration. It is effective with high accuracy and robustness under different penetration states.