Variable-scale acoustic texture image and interpretable filter convolutional networks for defect monitoring in laser powder bed fusion

The widespread application of Laser Powder Bed Fusion (LPBF) has led to an increasing interest in the process monitoring technologies. However, the common method of combining signal sensing and machine learning (ML) in LPBF defect monitoring faces two primary challenges: (1) varying laser scan speeds lead to data imbalance due to differing amounts of data collected per unit distance. (2) the widely used convolutional neural networks lack interpretability. In view of the above limitations, this paper proposes a defect monitoring method of variable-scale acoustic texture image and interpretable texture convolution. First, based on the typical characteristics of LPBF acoustic signals, this method can represent relevant physical information in the form of texture, and guide scale design in combination with process parameters. Secondly, based on the advanced texture filter function as the underlying architecture, the interpretable texture kernel convolution is extended and designed. The acoustic texture image designed in combination with processing parameters can characterize the frequency information of the LPBF process, and the interpretable texture convolution makes the feature extraction interpretable. Finally, the effectiveness of the method is verified on the LPBF defect dataset. The results show that the acoustic texture image can effectively represent process information. The interpretable texture convolution achieves interpretable feature mapping, which performs better in terms of parameter quantity, convergence speed and accuracy. In addition, the operation mode of the proposed method is verified through visual analysis.