Abstract:
Predicting features of mechanical deformation represents a fantastic challenge as it depends on a myriad of factors related to material microstructure, specimen geometry and loading conditions. Micromechanical approaches try to bridge microstructural features of solids with their mechanical response, but they are confronted to the high level of complexity of microstructures and microscopic damage processes involved in materials. Recently, new approaches inspired from data science have emerged: the key microscale parameters that control mechanical deformation at large scale can be identified, and even learned, from the statistical analysis of a large amount of data. Statistical predictions range in a wide gamut of applications, from indentation of composites, to neural networks for understanding of elastic and plastic features of deformation in crystals. I will present a set of tools, based on principal component analysis, wavelets and machine learning, that utilize the dynamical stability of elasticity as an asset towards microstructural characterization and parameter-free multiscale modeling of key mechanical properties, such as the yield point. Through this approach, I will demonstrate applications for synthetic data in binary alloys and polycrystals, and will discuss experimental validation routes by using the digital image correlation (DIC) method.
You will find the complete schedule for Njord Seminar Series spring '21 here.
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