Surface hardness monitoring of laser shock Peening: Acoustic emission and key frame selection

Acoustic emission (AE) technology has great potential in online monitoring of laser shock peening (LSP). Still, its high sampling frequency leads to a large amount of real-time calculation, posing a great challenge to the industrial application of monitoring technology. Attention weight statistics (AWS) is proposed to obtain the key frames of AE signals in LSP processing to solve this problem. Compared with the original AE signal, key frames set of the signal provide greater test accuracy while effectively reducing the amount of data. Based on the highest accuracy and the shortest test time of key frames set, the best sensors of signal acquisition in four different sensors are evaluated, and the results can be used as a reference for future experiments. Finally, the physical significance of AE signal key frames is explained with time–frequency domain analysis.