Colour-Field Based Particle Categorization for Residual Stress Detection and Reduction in Solid SPH Simulations

Gizem Kayar

2023

Abstract

Residual stress remains in an object even in the absence of external forces or thermal pressure, which, in turn, may cause significant plastic deformations. In case the residual stress creates unwanted effects on the material and so is undesirable, an efficient solution is necessary to track and eliminate this stress. Smoothed Particle Hydrodynamics has been extensively used in solid mechanics simulations and the inherent colour-field generation approach is a promising tracker for the residual stress. In this paper, we propose a way to use the colour-field approach for eliminating the residual stress and prevent the undesirable premature failure of solid objects.

Paper Citation

in Harvard Style

Kayar G. (2023). Colour-Field Based Particle Categorization for Residual Stress Detection and Reduction in Solid SPH Simulations. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 1: GRAPP; ISBN 978-989-758-634-7, SciTePress, pages 237-241. DOI: 10.5220/0011716100003417

in Bibtex Style

@conference{grapp23,
author={Gizem Kayar},
title={Colour-Field Based Particle Categorization for Residual Stress Detection and Reduction in Solid SPH Simulations},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 1: GRAPP},
year={2023},
pages={237-241},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011716100003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 1: GRAPP
TI - Colour-Field Based Particle Categorization for Residual Stress Detection and Reduction in Solid SPH Simulations
SN - 978-989-758-634-7
AU - Kayar G.
PY - 2023
SP - 237
EP - 241
DO - 10.5220/0011716100003417
PB - SciTePress