Artificial Neural Network Approach to Predict the Elastic Modulus from Dynamic Mechanical Analysis Results

Characterizing viscoelastic materials over a range of temperatures and strain rates requires an elaborate experimental scheme. Methods are available that allow testing a single specimen and then transform the storage modulus data to recover elastic modulus. However, applying these methods to polymers with more than one thermal transitions is challenging. As the form of artificial neural network (ANN) follows the time–temperature superposition principle, it is used in the present work to establish the master relation for storage modulus. The neural network is built with various neuron numbers and regularization factors, and two magnitudes of Gaussian noises. The predictions achieve the best accuracy when the regularization factor equals 10−4 and neuron number equals the number of thermal transitions in the material. Then the ANN is trained on storage modulus data of graphene‐epoxy nanocomposites and achieves 4.1% average error. The storage modulus is transformed to time domain relaxation function using integral relation of viscoelasticity. Stress response over the strain history is determined and the elastic modulus is extracted. Compared to the tensile test results, the predictions achieve an average error of 0.7%, which indicates that the method can predict the material behavior over a wide range of temperatures and strain rates. By combining the artificial neural network and viscoelastic theory, a transformation to recover the time domain elastic modulus from dynamic mechanical analysis results is developed. The method can predict the properties of complex material systems under a wide range of temperatures and strain rates from a single specimen without the need for conducting numerous tensile tests.