期刊
INTERNATIONAL JOURNAL OF PLASTICITY
卷 135, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijplas.2020.102811
关键词
Machine learning; Viscoplasticity; Polypropylene; Neural network; Automated testing
资金
- MIT/ETH Industrial Fracture Consortium
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 degrees 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.
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