Weld bead penetration identification based on human-welder subjective assessment on welding arc sound

Utilizing arc sound signals to monitor the penetration states of weld bead is a promising method. However, the correlation between penetration states and arc sounds is relatively weak, and arc sounds easily affected by background noise. To distinguish the penetration states of weld bead from arc sound signals, a novel method was developed in this paper. This method utilized the subjective assessments of human welders on arc sounds to build mathematical model, and to identify penetration states. Firstly, a dissimilarity matrix of arc sound pairs was made according to the subjective assessments of human welders on these sound pairs. Subsequently, the correlations of penetration states with auditory features of arc sounds were obtained based on this dissimilarity matrix. Finally, a support vector machine was built to identify the penetration states. The results show that the proposed method has a high correct rate in identifying the penetration states of weld bead. © 2020 Elsevier Ltd