Welding penetration recognition based on arc sound and electrical signals in K-TIG welding. (15th October 2020)

• Record Type: Journal Article
• Authors: Cui, Yanxin; Shi, Yonghua; Zhu, Tao; Cui, Shuwan
• Is Part Of: Measurement. Volume 163(2020)
• Publication Date: 2020-10-15
• Subjects: K-TIG welding -- Multisensor sensing -- Weld penetration -- SVM; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.107966
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: The Spectral Noise Subtraction method was introduced to denoise the arc sound. The excitation source of arc sound was revealed. The relationship between arc sound and arc current was proposed. The interference of electromagnetic field on acoustic sensor can be ignored. A welding penetration recognition model was obtained with an accuracy of 98.7%. 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.