Data generation and deep neural network predictions for aged mechanical properties

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Abstract The aim of this work is the data generation of aged mechanical properties following the Arrhenius equation and large deformation theory for a transversely isotropic bio‐based polyurethane foam, and the application of this dataset in the training process of different deep neural network architectures to evaluate their capacity to predict the full stress–strain behavior of this material after being exposed to different temperatures and long periods of time. To investigate the transversely isotropic behavior, the mechanical properties were divided into two, longitudinal and transverse to the expansion direction, and a total of 4200 simulations with different temperature and age parameters were used in a UMAT subroutine calculating the stress by the Jaumann stress rate for logarithmic strain levels of up to 1.2 mm/mm. The strain level obtained by the simulation, as well as the material direction, temperature, and time of exposure were used as input parameters in the DNN, with the output being stress level, yield strength, and stiffness. Tensorflow library was used to model the DNN with two and three hidden layers of depth and width varying from 128 to 1024 neurons. We also investigated the differences between using the ReLU and ELU activation functions in this problem. Our findings highlight the impressive predictive capabilities of DNNs, with the ReLU‐512‐512‐512 architecture demonstrating superior performance in terms of accuracy and computational efficiency, reaching error values for the stress prediction of 5.5 kPa. Overall, this research underscores the potential of DNNs as a cost‐effective and efficient tool for predicting material behavior, offering valuable insights for researchers and engineers across various fields. Highlights We created and validated thousands of simulations of aged stress–strain curves. We made our simulation dataset available. We evaluated varied network architectures and functions and ranked the best for our case. We validated predictions against simulations and experiments. We demonstrated DNNs' efficacy in predicting aged mechanical properties.

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