通过声学信号与深度学习实现激光定向能量沉积中原位裂纹和匙孔气孔检测
In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning
AI总结：
本文首次提出基于声学信号和卷积神经网络的激光定向能量沉积原位缺陷检测方法，通过去噪、特征提取和MFCC分类实现裂纹与匙孔气孔在线预测，总体准确率达89%。
摘要：
裂纹和匙孔气孔是激光定向能量沉积（LDED）制备合金中的有害缺陷。激光与材料相互作用的声音可能包含关于裂纹扩展和气孔形成等复杂物理事件的信息。然而，由于环境噪声大且信号内容复杂，基于声学的LDED监测一直很少受到关注。本文提出了一种新颖的基于声学的LDED原位缺陷检测策略。本研究的关键贡献是开发了一套原位声学信号去噪、特征提取和声音分类流程，该流程结合了卷积神经网络（CNN）用于在线缺陷预测。使用显微镜图像识别零件内裂纹和匙孔气孔的位置。缺陷位置与声学信号进行时空配准。提取并分析了与无缺陷区域、裂纹和匙孔气孔相对应的各种声学特征，包括时域、频域和时频表示。CNN模型使用激光与材料相互作用声音的梅尔频率倒谱系数（MFCCs）进行训练，以预测缺陷的发生。将该CNN模型与在去噪声学数据集和原始声学数据集上训练的各种经典机器学习模型进行了比较。验证结果表明，在去噪数据集上训练的CNN模型优于其他模型，具有最高的总体准确率（89%）、匙孔气孔预测准确率（93%）和AUC-ROC得分（98%）。此外，训练好的CNN模型可以部署到内部开发的软件平台中，用于在线质量监测。所提出的策略是首次在LDED工艺中使用声学信号与深度学习进行原位缺陷检测的研究。
英文摘要：
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.
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