Abstract Monitoring weld condition using acoustic method is quite challenging due to some factors, hence the importance to further explore the use of signal analysis method not only to diminish the effect of noise, but more importantly, to obtain a distinct correlation with weld condition. The main goal of this work is to determine the significance of the feature extracted from synchrosqueezed-wavelet analysis in classifying sound signals that derived from varied weld penetration conditions. In achieving the aim, sound signal was acquired during pulse mode laser welding process with variation in peak power and pulse width that produced half penetration and full penetration weld joints. The trends of time domain, frequency domain, and wavelet analysis features of the acquired sound from half and full penetration welds were compared prior to the support vector machine (SVM) classification analysis. The comparison between all the features displayed a clear distinction in signals between half and full penetration welds for the case of features extracted from synchrosqueezed-wavelet analysis. In SVM binary classification analysis, the use of same feature as input recorded 96.94 % of average classification accuracy, which appeared to be the highest. Additionally, comparison with band power, exhibited 27.7 % improvement in classification precision. From these findings, it is concluded that the use of features extracted from synchrosqueezed-wavelet analysis as input for classification of sound signals from various penetration conditions is indeed significant. This work contributes to an alternative way in dealing with random sound signals in view of developing an efficient weld penetration monitoring system in future.

中文翻译：

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通过对脉冲模式激光焊接过程中获取的声音信号进行同步压缩小波分析来对焊缝熔深状况进行分类

摘要 由于某些因素，使用声学方法监测焊缝状态具有挑战性，因此进一步探索使用信号分析方法不仅可以减少噪声的影响，更重要的是获得与焊缝状态的明显相关性。这项工作的主要目标是确定从同步压缩小波分析中提取的特征在对来自不同焊缝熔深条件的声音信号进行分类时的重要性。为了实现这一目标，在脉冲模式激光焊接过程中获取声音信号，峰值功率和脉冲宽度变化，产生半熔透和全熔透焊接接头。时域、频域的趋势，在支持向量机 (SVM) 分类分析之前，比较了从半焊缝和全焊缝获得的声音的小波分析特征。对于从同步压缩小波分析中提取的特征，所有特征之间的比较表明半熔透焊缝和全熔透焊缝之间的信号有明显区别。在 SVM 二元分类分析中，使用与输入相同的特征记录了 96.94% 的平均分类准确率，这似乎是最高的。此外，与频段功率相比，分类精度提高了 27.7%。从这些发现中可以得出结论，使用从同步压缩小波分析中提取的特征作为来自各种穿透条件的声音信号分类的输入确实很重要。