LAUSR

Abstract This paper presents a technology for welding quality control in pulse gas tungsten arc welding (GTAW). An automated welding penetration control system and effective controller are designed to achieve real-time collection and analysis of the welding acoustic signal. A special preprocessing method called “auditory attention” is proposed to optimize the extraction of the arc sound signal, which includes region of interest (ROI) extraction and denoising. The penetration feature extraction is implemented in the preprocessed signal. A sound channel feature based on linear prediction cepstrum coefficient (LPCC) is proposed for inclusion in the feature extraction method. Using these penetration features, a typical back propagation artificial neural network (BPANN) prediction model is introduced for identification of the penetration state during the welding process. Through training using a large number of data, the prediction rate reached 80–90%. The BPANN-piecewise (BPANN-PW) controller is used to achieve online control of welding penetration via arc sound signal for pulse GTAW welding using work-piece of different shapes. The results showed that this controller could adjust the welding current accurately and promptly depending on the variation of the arc sound signal. The controlling effect was good for the online monitoring of automated robotic GTAW welding.