Prediction of void evolution in sheet bending based on statistically representative microstructural data for the Gurson-Tvergaard-Needleman model
Abstract
Ductile damage in sheet steels is caused by voids. It is crucial for product design to predict the distribution of voids in bent components. Since the void volume fraction is a state variable in the Gurson-Tvergaard-Needleman (GTN) model, it is applied to predict the evolution of voids in bending. Material parameters are identified based on force-displacement curves of a dual phase steel and also through statistical microstructural information obtained from panoramic scanning-electron microscopy images. The void volume fraction and particular void populations of GTN-model are determined with a recently proposed scheme, which involves machine learning algorithms.
Cite
@article{arxiv.2006.15973,
title = {Prediction of void evolution in sheet bending based on statistically representative microstructural data for the Gurson-Tvergaard-Needleman model},
author = {Alexander Schowtjak and Carl F. Kusche and Rickmer Meya and Sandra Korte-Kerzel and Talal Al-Samman and A. Erman Tekkaya and Till Clausmeyer},
journal= {arXiv preprint arXiv:2006.15973},
year = {2020}
}
Comments
Proceedings of NUMIFORM 2019: The 13th International Conference on Numerical Methods in Industrial Forming Processes; Reviewed by Scientific Committee; 4 pages, 3 figures; otherwise not publicly accessible