| DU Zheng-yao,ZHANG Zhi-fen,QIN Rui,LI Geng,WEN Guang-rui,HE Wei-feng.Surface Hardness Monitoring of Laser Shock Peening:Acoustic Emission and Key Frame Selection[J],51(11):35-44 |
| Surface Hardness Monitoring of Laser Shock Peening:Acoustic Emission and Key Frame Selection |
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| DOI：10.16490/j.cnki.issn.1001-3660.2022.11.004 |
| KeyWord:surface hardness laser shock peening acoustic emission key frame LSTM |
| Author | Institution |
| DU Zheng-yao |
Xi'an Jiaotong University, Xi'an , China |
| ZHANG Zhi-fen |
Xi'an Jiaotong University, Xi'an , China |
| QIN Rui |
Xi'an Jiaotong University, Xi'an , China |
| LI Geng |
Xi'an Jiaotong University, Xi'an , China |
| WEN Guang-rui |
Xi'an Jiaotong University, Xi'an , China |
| HE Wei-feng |
Air Force Engineering University, Xi'an , China |
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| Abstract: |
| Laser Shock Peening (LSP) is a new technology which can improve the surface hardness of target and change the surface properties of metal materials. It has the advantages of good controllability and high processing efficiency, and is widely used in surface strengthening treatment of key components such as aero-engine blades. In order to overcome the problems of long cycle and low efficiency of off-line methods such as hardness testing, it is urgent to improve the on-line testing ability of laser shock strengthened surface hardness, improve the reliability and consistency of LSP processing quality while improving the quality testing efficiency, and ensure the service performance of aviation equipment. During LSP treatment, Acoustic Emission (AE) has dynamic information closely related to lattice dislocation and plastic deformation in the material, and it is a promising method for on-line monitoring of LSP. Therefore, we can identify the surface hardness of LSP by exploring the information of AE signal. In this paper, the surface hardness of 7 kinds of samples under different laser energy impacts is collected through off-line characterization experiments, and it is used as the basis for online detection and classification. However, the high sampling frequency of AE signals leads to a large number of real-time calculations, which poses a huge challenge to the industrial application of online monitoring technology. To solve this problem, attention weight statistics (AWS) method is proposed to obtain the key frames of acoustic emission signals during LSP. The time dimension weight of attention mechanism in LSTM model is visualized. After collecting a large number of attention weight samples, we find that some categories of attention weight samples are multi-peak, and multiple peak positions average the weight distribution, while some categories of attention weight samples are single-peak, and the peak positions cover most of the weights. Faced with the different weight distribution characteristics of each category, AWS method can comprehensively reflect the attention weight of each category of AE signals, and can be used as a reference for extracting key frames. Compared with the effective length of the original signal, the four-channel sensor's key frame signal length can be greatly reduced by 83.74% at the maximum, and the test time can be reduced by 57.37% at the maximum compared with each round of testing of the original data (350 AE samples). The model classification accuracy of key frame signals can reach 97.04%, which is 2.93% higher than the original data set. 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. The method proposed in this paper is affected by the number of experimental samples, and at present it can only represent the detection effect of the network model on the existing data. Although the quantitative results will change with the change of experimental conditions, the method itself has certain universality, and the results can be used as a reference in the field of LSP quality monitoring. |
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