IRTG Modern Inverse Problems (MIP)
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SSD Seminar Series with Binbin Lin, M. Sc. and Dr. Daniel Utt and Setareh Medghalchi, M. Sc.

Januar 31 @ 16:00 - 17:00

Binbin Lin, M. Sc. and Dr. Daniel Utt and Setareh Medghalchi, M. Sc. – Computational and Experimental Methods in Material Science

Simulation Data Lab Material Design, Technical University of Darmstadt and RWTH Aachen University


Parallel Extraction of Dislocations from Atomic Arrangements  by Dr. Daniel Utt


Atomistic computer simulations give unique insights into the functional and mechanical properties of a wide range of materials. One-dimensional linear defects, so-called dislocations, play a crucial role in governing these properties and are therefore of great interest to the materials science community. However, dislocations are not discrete objects, like particles or even vacancies, instead, they are displacement fields collectively shifting the atoms from their ideal lattice sites. Atomistic simulations do not track these dislocations directly, instead they simulate the trajectories of the atoms and dislocations form as a natural response to external perturbation. This however means that there is no immediate way to extract the dislocation lines and densities from such a virtual sample. Instead, the atomic displacements need to be analyzed to find them. The two established methods come with their own advantages and disadvantages. Nevertheless, neither of them is sufficiently fast to be applied to large scale atomistic simulations as part of the actual simulation process and a new approach is needed to extract dislocation lines on-the-fly. Here we show a new approach based on the Delaunay triangulation of the atomic positions. Each edge in the triangulation is assigned an ideal crystal lattice vector and dislocations are found from incompatibilities of these ideal lattice vectors in adjacent triangles. As this method operates on individual Delaunay cells, it can be spatially discretized and run in parallel on a large number of processors.


Experimental and numerical analysis of Dual-Phase Steel: an integrated approach using machine learning  by Setareh Medghalchi, M. Sc. and Binbin Lin, M. Sc.


In materials engineering, dual-phase (DP) steels are a class of advanced alloys that are frequently used in automotive applications due to their good specific mechanical properties and ductility allowing lightweight design. Microstructural damage can occur during metal forming, but how and where this happens vary with the local microstructure and strain path. Large-scale analysis of such damage mechanisms is particularly important in advanced steels with a heterogeneous phase distribution. In this talk, an in-depth large-scale microstructure analysis by paranomic imaging method is presented. Along with a trained deep learning model, different phases in DP steel with various types of fracture are segmented and analyzed. Further, the deep learning segmented phase information are passed on, as basis to the imaged-based finite-element (FE) simulation. The obtained microstructure information is processed, discretized in FE-mesh, and modelled as elasto-plastic materials to study the mechanical behavior and the microstructure-property relationship.


Januar 31
16:00 - 17:00


a link for the Zoom meeting room will be send in the newsletter one week before the seminar starts. If you need any organizational help please contact office@aices.rwth-aachen.de