The data-driven method based on acoustic emission signals is gradually becoming a hot topic in the field of laser shock peening quality monitoring. Although some existing deep learning methods do provide excellent monitoring accuracy and speed, they lack physical interpretability in nature, and the opacity of these decisions poses a great challenge to their credibility. The weak interpretability of deep learning models has become the biggest obstacle to the landing of artificial intelligence projects. To overcome this drawback, this paper proposes a monitoring strategy that can achieve physical interpretability in feature extraction, selection and classification, namely, jointly generating monitoring results and explanations. Specifically, it is an end-to-end model that combines convolutional neural units, gated recurrent units, and attention mechanisms. Firstly, a wavelet analysis with physical meaning that can be autonomously learned is performed on the acoustic emission. Then, the contribution of features is distinguished based on the correlation of information in different frequency bands, and redundant and noisy features are removed. Finally, the interpretability evaluation of processing quality is realized by using gated recurrent units with attention mechanisms. The effectiveness and reliability of the proposed method are confirmed by the experimental data of both laser shock peening at small and large gradient energies compared to state-of-the-art feature methods, CNN- and LSTM-based models. Most importantly, the physical interpretation of acoustic emission signals during the processing can increase the credibility of decisions and provide a basic logic for on-site judgments by professionals.

基于声发射信号的数据驱动方法逐渐成为激光冲击喷丸质量监测领域的热点。尽管现有的一些深度学习方法确实提供了出色的监测精度和速度，但它们本质上缺乏物理可解释性，而且这些决策的不透明性对其可信度构成了巨大挑战。深度学习模型的可解释性较弱成为人工智能项目落地的最大障碍。为了克服这一缺点，本文提出了一种监测策略，可以在特征提取、选择和分类方面实现物理可解释性，即联合生成监测结果和解释。具体来说，它是一个结合了卷积神经单元、门控循环单元和注意力机制的端到端模型。首先对声发射进行具有可自主学习物理意义的小波分析。然后，根据不同频段信息的相关性来区分特征的贡献，去除冗余和噪声特征。最后，通过使用具有注意机制的门控循环单元实现了处理质量的可解释性评估。与最先进的特征方法、基于 CNN 和 LSTM 的模型相比，小梯度能量和大梯度能量激光冲击喷丸的实验数据证实了该方法的有效性和可靠性。最重要的是，声发射信号在处理过程中的物理解释可以增加决策的可信度，为专业人士的现场判断提供基本逻辑。