Neural network model describing the temperature- and rate-dependent stress-strain response of polypropylene. (December 2020) - Jordan, Benoit; Gorji, Maysam B.; Mohr, Dirk
Abstract: A machine learning based model is proposed to describe the temperature and strain rate dependent response of polypropylene. A hybrid modeling approach is taken by combining mechanism-based and data-based modeling. The "big data" required for machine learning is generated using a custom-made robot-assisted testing system. Numerous large deformation experiments are performed on mildly-notched tensile specimens for temperatures ranging from 20 to 80 °C, and strain rates ranging from 10 −3 to 10 −1 /s. Without making any a priori assumptions on the specific mathematical form, the function relating the stress to the viscous strain, the viscous strain rate and temperature is identified using machine learning. In particular, a back propagation algorithm with Bayesian regularization is employed to identify a suitable neural network function based on the results from more than 40 experiments. The neural network model is employed in series with a temperature-dependent spring to describe the stress-strain response of polypropylene. The resulting constitutive equations are solved numerically to demonstrate that the identified model is capable to predict the experimentally-observed stress-strain response for strains of up to 0.6. Highlights: Developed robot-assisted testing system for creating large experimental database. Used machine learning to analyze experimental data. Proposed neural network function to describe relationship among viscous strain, strain rate, temperature and stress. Trained and validated constitutive model for polypropylene.