Research on Neural Network Based Strain Field Inversion Method for Composite Materials
In order to solve the problem that it is difficult to predict the load at the key position of the aircraft skin,it is diffi-cult to realize the global strain field monitoring of the structure.In this paper,a strain field reconstruction method based on deep neural network was proposed.According to the fragile characteristics of aircraft skin,a perforated test piece was designed,and a strain acquisition system was built by a fatigue testing machine and strain gauges.In order to solve the problem of error between the finite element model and the real response,the least squares method was used to correct the finite element analysis results.The neural network training dataset was established,and the multi-layer algorithm was used to optimize the neural network archi-tecture and parameters for each dataset,which realized the exact equivalence from structure to strain.The results show that the prediction accuracy of the strain field reconstruction model is more than 90%.The global strain reconstruction theory from the lo-cal finite measurement point to the global structure is realized.
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