Identification of stress damage in poplar plywood based on AE-BP model
[Objective]Acoustic emission(AE)is used to detect the damage of poplar plywood in the whole process of stress damage,and BP neural network is applied to identify the results of AE,so as to improve the damage detection accuracy of plywood.[Method]Poplar plywood used in pallets with high market share was taken as the research object.During the joint AE and stress damage test,six AE characteristic parameters were extracted,the crack types of plywood were distinguished by acoustic emission RA-AF joint analysis method,and the corresponding relationship between damage evolution degree and AE characteristic parameters was determined by K-means clustering analysis method.The damage identification model was established by BP neural network,and the identification network was trained by test.[Result]AE signal amplitude and rise time effectively characterized the evolution process of stress damage from microcrack initiation,macroscopic crack to complete fracture;Through RA-AF analysis,it was found that in the first stage of bending test,the main damage of poplar plywood is shear failure.In the second and third stages,the main damage was tensile failure;Based on cluster analysis,it was found that there was a strong corresponding relationship between damage types and AE Peak frequencies,the different damage patterns could be effectively characterized:matrix cracking within 31 kHz,debond and delamination within 31-100 kHz,and fiber fracture above 100 kHz;The AE-BP neural network model could identify the fuzzy damage types with the goodness of fit of the training samples was 95.94%,the goodness of fit of the test set was 98.89%,and the total goodness of fit of the model was 96.51%,and the network training was more effective.[Conclusion]The damage types of poplar plywood during AE monitoring can be effectively detected and accurately identified by constructing AE-BP model.
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