Monitoring of weld joint quality is a significant issue in Cold Metal Transfer (CMT) lap welding. In this paper, CMT lap welding experiment of low carbon steel sheet was carried out, the sound characteristics of CMT are studied. Further analysis of the “special two-step mode” of welding sound shows that the faster the change of arc energy, the greater the corresponding sound pressure value. Furthermore, the feature extraction and fusion methods of welding electrical parameters and welding sound signals were investigated based on the two abnormal welding states: gas feeding error and welding wear. In the aspect of electric signal, welding current, welding voltage, line energy were studied, and in the aspect of sound signal, MFCC is extracted after de-framing and windowing. BiLSTM-CTC algorithm has been used to identify welding process gas feeding error and welding wear. For the recognition model the classification error rate based on sound feature is the lowest at 0.389, and the classification error rate based on electrical signal and acoustic signal fusion feature is at 0.295.

焊接接头质量的监控是冷金属转移 (CMT) 搭接焊中的一个重要问题。本文对低碳钢板进行了CMT搭接焊实验，研究了CMT的声学特性。进一步分析焊接声音的“特殊两步模式”可知，电弧能量的变化越快，相应的声压值就越大。进一步研究了基于供气错误和焊接磨损两种异常焊接状态的焊接电参数和焊接声音信号的特征提取和融合方法。在电信号方面对焊接电流、焊接电压、线能量进行了研究，在声音信号方面进行了去帧加窗后提取MFCC。BiLSTM-CTC 算法已被用于识别焊接过程气体供给错误和焊接磨损。对于识别模型，基于声音特征的分类错误率最低，为0.389，基于电信号和声学信号融合特征的分类错误率为0.295。