Deep learning based nanoindentation method for evaluating mechanical properties of polymers

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.

Automated summary

This study proposes a deep learning based nanoindentation method to simplify the evaluation of mechanical properties of polymers. A database is generated for a set of Drucker-Prager model parameters, representing the material behavior of polymers under nanoindentation. A deep neural network (DNN) is trained using optimized hyper-parameters identified through Bayesian hyperparameter tuning process. The performance of the trained DNN model is validated by experimental nanoindentation tests, accurately predicting the material parameters in good agreement with the literature.