A novel physically interpretable end-to-end network for stress monitoring in laser shock peening

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.