Abstract
In this study, a deep learning based nanoindentation method is proposed to reduce the complexities in evaluating mechanical properties of polymers. To uniquely identify the material parameters, a set of nanoindentation simulations are performed by employing spherical and Berkovich tips. A database that represents the material behavior of polymers under nanoindentation is generated for a set of Drucker-Prager model parameters. A deep neural network (DNN) is trained based on optimized hyper-parameters identified through Bayesian hyperparameter tuning process. The performance of trained DNN model is experimentally validated by performing nanoindentation tests on PC and PMMA. From nanoindentation load-depth (P-h) data, the trained DNN model accurately predicts the material parameters, which are in good agreement with those in the literature.
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