Using Deep Learning to Identify the Elastic Properties of Composite Materials from Vibration Data

The vibration-based identification method has long been used as an alternative to mechanical tests for material characterization. Its non-destructive nature, high efficiency in parameter identification of anisotropic materials and its ability to achieve structure-level homogenization make it very attractive for characterization of composite materials. Due to the absence of explicit mapping from the result of vibration test to elastic constants, conventional vibration-based identification approaches generally rely on the minimization of a cost function built on modal properties or Frequency Response Function (FRF) curve through massive iterations. In this paper, a direct relation from FRFs to mechanical properties is established by deep neural network, which offers a new perspective to solve the identification problem. Specifically, this mapping is approximately built with Multilayer Perceptrons (MLPs) and the network's weights are learned from massive training data. The training data-i.e., the FRF curves, is generated by a finite element model whose input parameters are selected by Latin Hypercube Sampling (LHS). After training, the obtained model is validated by datasets unseen during training process. The identification of mechanical properties of an orthotropic plate from synthetic data is presented and both training loss and validation loss are acceptable.