Arc sound model for pulsed GTAW and recognition of different penetration states

Abstract

Weld defect detection based on arc sound signal in pulsed gas tungsten arc welding (GTAW) has been a hot research topic in industry and academia. However, arc vocal mechanism model of pulsed GTAW remains to be studied further. In this paper, a sensing system is developed to collect arc voltage, welding current, arc sound, and weld pool images synchronously during pulsed GTAW process. Theoretical researches and experiments verify that the arc sound signal of pulsed GTAW is proportional to the change rate of the instantaneous arc power input. Furthermore, an arc sound excitation model is proposed to explain the mechanism of arc sound signal. The model inputs are arc voltage and welding current; the model output is the arc sound signal. The experiments prove that the proposed excitation model is a non-linear model. Therefore, a dynamic long short-term memory (DLSTM) network model is designed to identify the non-linear model. Furthermore, the effects of different penetration states on arc sound signal are discussed in combination with the proposed arc sound excitation model. Finally, a novel method based on DLSTM model is built to recognize different penetration states: lack of fusion, normal penetration, and burn through. The proposed method was verified to be effective with high accuracy and robustness.