Overheating detection in the powder bed fusion of metals using a laser beam through local thermal resistance analysis
Publication type: Journal Article
Publication date: 2024-10-09
Abstract
The powder bed fusion of metals using a laser beam is a production technique to additively manufacture parts by melting metallic powder layers with a laser. The geometry of a part affects how the heat is dissipated from the process zone. Geometry-induced heat accumulation results in undesirable processing conditions and part properties. Current approaches to identify locations of overheating are mostly based on thermal finite element simulations. This study presents an analytical approach to detect these locations based on a voxel-wise calculation of the local thermal resistance. With a thermal simulation, it was verified that the presented approach efficiently detects areas of overheating. This allows the automated evaluation of the manufacturability of existing geometries.
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Total citations:
2
Citations from 2024:
2
(100%)
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Wenzler D. L. et al. Overheating detection in the powder bed fusion of metals using a laser beam through local thermal resistance analysis // Procedia CIRP. 2024. Vol. 126. pp. 591-596.
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Beuerlein K., Rauner D. Overheating detection in the powder bed fusion of metals using a laser beam through local thermal resistance analysis // Procedia CIRP. 2024. Vol. 126. pp. 591-596.
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TY - JOUR
DO - 10.1016/j.procir.2024.08.245
UR - https://linkinghub.elsevier.com/retrieve/pii/S2212827124008138
TI - Overheating detection in the powder bed fusion of metals using a laser beam through local thermal resistance analysis
T2 - Procedia CIRP
AU - Beuerlein, Kai-Uwe
AU - Rauner, Dominik
PY - 2024
DA - 2024/10/09
PB - Elsevier
SP - 591-596
VL - 126
SN - 2212-8271
ER -
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@article{2024_Wenzler,
author = {Kai-Uwe Beuerlein and Dominik Rauner},
title = {Overheating detection in the powder bed fusion of metals using a laser beam through local thermal resistance analysis},
journal = {Procedia CIRP},
year = {2024},
volume = {126},
publisher = {Elsevier},
month = {oct},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2212827124008138},
pages = {591--596},
doi = {10.1016/j.procir.2024.08.245}
}