A thermo-mechanical DEM framework for the simulation of selective laser sintering (2022)
- Authors:
- USP affiliated authors: CAMPELLO, EDUARDO DE MORAIS BARRETO - EP ; RUIZ, OSVALDO DARIO QUINTANA - EP
- Unidade: EP
- Subjects: TERCEIRA DIMENSÃO; SIMULAÇÃO DE SISTEMAS; MECÂNICA DAS ESTRUTURAS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: ABMEC
- Publisher place: Rio de Janeiro
- Date published: 2022
- Source:
- Título: Proceedings
- Conference titles: Ibero-Latin-American Congress on Computational Methods in Engineering - CILAMCE
-
ABNT
QUINTANA RUIZ, Osvaldo Dario e CAMPELLO, Eduardo de Morais Barreto. A thermo-mechanical DEM framework for the simulation of selective laser sintering. 2022, Anais.. Rio de Janeiro: ABMEC, 2022. Disponível em: https://repositorio.usp.br/directbitstream/800f8983-eb63-43ba-83b7-d3eecfb589ca/A_thermo_mechanical_DEM_framework_for_the_simulation_of_selective_laser_sintering.pdf. Acesso em: 10 fev. 2026. -
APA
Quintana Ruiz, O. D., & Campello, E. de M. B. (2022). A thermo-mechanical DEM framework for the simulation of selective laser sintering. In Proceedings. Rio de Janeiro: ABMEC. Recuperado de https://repositorio.usp.br/directbitstream/800f8983-eb63-43ba-83b7-d3eecfb589ca/A_thermo_mechanical_DEM_framework_for_the_simulation_of_selective_laser_sintering.pdf -
NLM
Quintana Ruiz OD, Campello E de MB. A thermo-mechanical DEM framework for the simulation of selective laser sintering [Internet]. Proceedings. 2022 ;[citado 2026 fev. 10 ] Available from: https://repositorio.usp.br/directbitstream/800f8983-eb63-43ba-83b7-d3eecfb589ca/A_thermo_mechanical_DEM_framework_for_the_simulation_of_selective_laser_sintering.pdf -
Vancouver
Quintana Ruiz OD, Campello E de MB. A thermo-mechanical DEM framework for the simulation of selective laser sintering [Internet]. Proceedings. 2022 ;[citado 2026 fev. 10 ] Available from: https://repositorio.usp.br/directbitstream/800f8983-eb63-43ba-83b7-d3eecfb589ca/A_thermo_mechanical_DEM_framework_for_the_simulation_of_selective_laser_sintering.pdf - A methodology to predict the effective thermal conductivity of a granular assembly using deep learning
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