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Direct Metal Laser Sintering (DMLS) is a complex production process including hosts of parameters and a multitude of physical phenomena, which make the simulation and modeling quite challenging. This work investigates the impact of modified printing parameters (e.g., hatch distance, laser power) on correlating material properties (e.g., Young's modulus, temperature gradient) of hardened aluminum specimens. The ultimate goal is to create a data model that enables data-driven and multi-physical optimization of mechanical components fabricated via DMLS. | Direct Metal Laser Sintering (DMLS) is a complex production process including hosts of parameters and a multitude of physical phenomena, which make the simulation and modeling quite challenging. This work investigates the impact of modified printing parameters (e.g., hatch distance, laser power) on correlating material properties (e.g., Young's modulus, temperature gradient) of hardened aluminum specimens. The ultimate goal is to create a data model that enables data-driven and multi-physical optimization of mechanical components fabricated via DMLS. | ||
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| + | == Abstract == | ||
| + | <pdf>Media:Draft_Sanchez Pinedo_6460769031893_abstract.pdf</pdf> | ||
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| + | == Full Paper == | ||
| + | <pdf>Media:Draft_Sanchez Pinedo_6460769031893_paper.pdf</pdf> | ||
Latest revision as of 16:06, 25 November 2022
Summary
Direct Metal Laser Sintering (DMLS) is a complex production process including hosts of parameters and a multitude of physical phenomena, which make the simulation and modeling quite challenging. This work investigates the impact of modified printing parameters (e.g., hatch distance, laser power) on correlating material properties (e.g., Young's modulus, temperature gradient) of hardened aluminum specimens. The ultimate goal is to create a data model that enables data-driven and multi-physical optimization of mechanical components fabricated via DMLS.
Abstract
Full Paper
Document information
Published on 24/11/22
Accepted on 24/11/22
Submitted on 24/11/22
Volume Young Investigators Initiative, 2022
DOI: 10.23967/eccomas.2022.154
Licence: CC BY-NC-SA license
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