针对窄间隙熔化极气体保护焊(Gas Metal Arc Welding，GM AW)焊道侧壁处局部咬边缺陷检测困难的问题，提出了一种基于电弧声信号特征提取与处理的咬边缺陷在线检测方法。通过分析正常、临界咬边和咬边这3种焊接状态的电弧形态和电弧声信号特征，证实坡口侧壁引起的电弧形态变化是影响电弧声信号变化的重要因素。在此基础上采用小波包时频分析，同时引入特征类间标准差作为评价指标，确定了能有效识别3种焊接状态的敏感特征。采用Sigmoid支持向量机和五折交叉验证建立预测模型，实验结果表明该模型能较好地实现3种焊接状态的预测分类，识别准确率达到96。0％。
Analysis and identification of arc acoustic signal characteristics of occlusion defects in narrow gap GMAW
Aiming at the problem of difficulty in detecting local occlusion defects at the side wall of narrow gap GMAW,an online detection method for occlusion defects based on arc acoustic signal feature extraction and processing was proposed.By analyzing the arc morphology and arc acoustic signal characteristics of normal,critical occlusion and occlusion,it was confirmed that the arc morphology change caused by the side wall of the groove was an important factor affecting the change of arc acoustic signal.On this basis,the time-frequency analysis of wavelet packets was used,and the standard deviation between feature classes was intro-duced as an evaluation index to determine the sensitive features that can effectively identify the three welding states.Sigmoid sup-port vector machine and five-fold cross-validation were used to establish a prediction model,and the experimental results show that the model can better realize the prediction classification of three welding states,and the recognition accuracy reaches 96.0％.
narrow gap GMAWarc acoustic signalocclusion defectstime-frequency analysis
万方数据
维普
