Cracks and keyhole pores are detrimental defects in alloys produced by laser directed energy deposition (LDED). Laser-material interaction sound may hold information about underlying complex physical events such as crack propagation and pores formation. However, due to the noisy environment and intricate signal content, acoustic-based monitoring in LDED has received little attention. This paper proposes a novel acoustic-based in-situ defect detection strategy in LDED. The key contribution of this study is to develop an in-situ acoustic signal denoising, feature extraction, and sound classification pipeline that incorporates convolutional neural networks (CNN) for online defect prediction. Microscope images are used to identify locations of the cracks and keyhole pores within a part. The defect locations are spatiotemporally registered with acoustic signal. Various acoustic features corresponding to defect-free regions, cracks, and keyhole pores are extracted and analysed in time-domain, frequency-domain, and time-frequency representations. The CNN model is trained to predict defect occurrences using the Mel-Frequency Cepstral Coefficients (MFCCs) of the lasermaterial interaction sound. The CNN model is compared to various classic machine learning models trained on the denoised acoustic dataset and raw acoustic dataset. The validation results shows that the CNN model trained on the denoised dataset outperforms others with the highest overall accuracy (89%), keyhole pore prediction accuracy (93%), and AUC-ROC score (98%). Furthermore, the trained CNN model can be deployed into an in-house developed software platform for online quality monitoring. The proposed strategy is the first study to use acoustic signals with deep learning for insitu defect detection in LDED process.

翻译：裂纹和匙孔型气孔是激光定向能量沉积（LDED）合金中常见的有害缺陷。激光与材料相互作用的声音可能蕴含裂纹扩展、气孔形成等复杂物理过程的信息。然而，由于环境噪声和信号内容复杂，基于声学监测的LDED研究鲜有报道。本文提出一种基于声学的LDED原位缺陷检测新策略。本研究的关键贡献在于开发了一种结合卷积神经网络（CNN）的原位声学信号去噪、特征提取及分类流程，用于在线缺陷预测。通过显微镜图像识别构件内裂纹和匙孔型气孔的位置，并将缺陷位置与声学信号进行时空配准。针对无缺陷区域、裂纹和匙孔型气孔，提取并分析了时域、频域及时频域的多类声学特征。利用激光-材料相互作用声的梅尔频率倒谱系数（MFCC）训练CNN模型以预测缺陷生成。将CNN模型与基于去噪声学数据集和原始声学数据集的多种经典机器学习模型进行对比。验证结果表明，基于去噪数据集训练的CNN模型综合性能最优，总体准确率达89%，匙孔型气孔预测准确率达93%，AUC-ROC得分为98%。此外，训练后的CNN模型可部署至自主开发的软件平台，实现在线质量监测。本研究首次将声学信号与深度学习相结合，用于LDED工艺的原位缺陷检测。