Deep learning-based monitoring of surface residual stress and efficient sensing of AE for laser shock peening

Laser shock peening (LSP) is one of the main anti-fatigue technologies in high-end manufacturing industries. However, guaranteeing its quality consistency is difficult due to its process complexity. Acoustic emission (AE) is a promising solution to accurately monitoring the surface quality of LSP, although, the large scale of monitoring data is still challenging for industrial applications. This paper studied the in-situ evaluation of surface residual compressive stress (SRCS) for 7075 aluminum alloy in LSP and the efficient sensing of AE based on deep learning methods. Firstly, a monitoring system of LSP with four types of AE sensors was developed in order to simultaneously acquire both the ultra-high and ultra-low amplitude of shock wave. The performance of those sensors is further quantitatively evaluated via long-short term memory (LSTM). Then, a new spatio-temporal parallel CNN-LSTM model for SRCS classification was proposed and experimentally validated to outperform CNN and LSTM after parameters optimization. Finally, aiming to efficient sensing of AE, the importance of different frames of AE signal was analyzed by means of the proposed dual circulation LSTM (DC-LSTM) model, which can accurately locate the key frame of time series signal. Besides, the effects of different frames of AE on classification performance was discussed. It was found that the broadband AE sensor without attenuator shows the highest classification accuracy, while the fast decay stage of AE signal has more contribution to the SRCS classification. This paper provides guidance for real-time non-destructive evaluation of residual stress via AE in laser manufacturing.