Reference : Identification of optimal process parameters in selective laser sintering
Scientific congresses, symposiums and conference proceedings : Unpublished conference
Engineering, computing & technology : Materials science & engineering
Computational Sciences
http://hdl.handle.net/10993/40242
Identification of optimal process parameters in selective laser sintering
English
Kabore, Brice Wendlassida mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Estupinan Donoso, Alvaro Antonio mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Peters, Bernhard mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Baroli, Davide []
2019
No
International
II International Conference on Simulation for Additive Manufacturing - Sim-AM 2019
11-13-09-2019
Pavia
Italy
[en] Additive manufacturing ; selective laser sintering ; thermodynamics
[en] Selective Laser Sintering (SLS) is an efficient method for manufacturing complex geometries with high strength and durability. The SLS process subjects a powder bed to thermal cycles allowing theparticles to coalesce into a solid part without being completely melted. The thermal cycles along withthe thermo-mechanical properties of the powder dictate the properties of the manufactured part.Choosing optimal parameters that lead to functional parts with the desired stiffness, density andstrength requires extensive testing. Microscales models such that Molecular dynamics and DiscreteParticles offer great flexibilities and capacity to reproduce the SLS process from the physical point ofview [1].This study presents a multi-physical model based on the Extended Discrete Element Method forsimulating the thermodynamics and thermo-mechanics that take place in the SLS process as well asthe microstructure evolution of the part. A thermo-viscoelastic constitutive model for discreteparticles is coupled with heat transfer, sintering and fracture to predict.A genetic algorithm is employed to identify optimal process parameters, namely laser power,scanning speed, preheating temperature and layer thickness in an automated iterative process. Theseparameters are identified so that the density and strength of the cooled part meet the target values.
http://hdl.handle.net/10993/40242

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