(One intermediate revision by the same user not shown)
Line 3: Line 3:
  
 
Additive Manufacturing (AM) has been a subject of significant attention from both industrial manufacturers and research communities. However, several challenges hinder the widespread implementation of this technology in the industry. Powder recoating is a crucial step in powder-bed AM process that involves achieving a uniformly packed bed of powder particles that are later melted by an energy source, such as a laser or electron beam. One of the main challenges is calibrating the contact model parameters accurately to match the flowability and spreadability of specific powder alloys. This paper proposes a Discrete Element Method (DEM) model calibration framework based on surrogate model optimisation. The study utilises a Revolution Powder Analyser (RPA) as the experimental reference system. The proposed method is demonstrated with two AM powder samples, Ti64 and Inconel 718. The results indicate that particle-particle friction, rolling resistance, and van der Waals (vdW) surface energy significantly affect the system responses. Furthermore, the validation results show good correspondence between the simulation with calibrated parameters and experimental data. Overall, proposed calibration framework has the potential to optimise powder recoating and to improve the accuracy and effectiveness of the additive manufacturing.
 
Additive Manufacturing (AM) has been a subject of significant attention from both industrial manufacturers and research communities. However, several challenges hinder the widespread implementation of this technology in the industry. Powder recoating is a crucial step in powder-bed AM process that involves achieving a uniformly packed bed of powder particles that are later melted by an energy source, such as a laser or electron beam. One of the main challenges is calibrating the contact model parameters accurately to match the flowability and spreadability of specific powder alloys. This paper proposes a Discrete Element Method (DEM) model calibration framework based on surrogate model optimisation. The study utilises a Revolution Powder Analyser (RPA) as the experimental reference system. The proposed method is demonstrated with two AM powder samples, Ti64 and Inconel 718. The results indicate that particle-particle friction, rolling resistance, and van der Waals (vdW) surface energy significantly affect the system responses. Furthermore, the validation results show good correspondence between the simulation with calibrated parameters and experimental data. Overall, proposed calibration framework has the potential to optimise powder recoating and to improve the accuracy and effectiveness of the additive manufacturing.
 +
 +
== Full Paper ==
 +
<pdf>Media:Draft_Sanchez Pinedo_957969061pap_94.pdf</pdf>

Latest revision as of 10:57, 23 November 2023

Abstract

Additive Manufacturing (AM) has been a subject of significant attention from both industrial manufacturers and research communities. However, several challenges hinder the widespread implementation of this technology in the industry. Powder recoating is a crucial step in powder-bed AM process that involves achieving a uniformly packed bed of powder particles that are later melted by an energy source, such as a laser or electron beam. One of the main challenges is calibrating the contact model parameters accurately to match the flowability and spreadability of specific powder alloys. This paper proposes a Discrete Element Method (DEM) model calibration framework based on surrogate model optimisation. The study utilises a Revolution Powder Analyser (RPA) as the experimental reference system. The proposed method is demonstrated with two AM powder samples, Ti64 and Inconel 718. The results indicate that particle-particle friction, rolling resistance, and van der Waals (vdW) surface energy significantly affect the system responses. Furthermore, the validation results show good correspondence between the simulation with calibrated parameters and experimental data. Overall, proposed calibration framework has the potential to optimise powder recoating and to improve the accuracy and effectiveness of the additive manufacturing.

Full Paper

The PDF file did not load properly or your web browser does not support viewing PDF files. Download directly to your device: Download PDF document
Back to Top

Document information

Published on 23/11/23
Submitted on 23/11/23

Volume Computational Modeling of Manufacturing Processes Using Particle and Meshless Methods, 2023
DOI: 10.23967/c.particles.2023.018
Licence: CC BY-NC-SA license

Document Score

0

Views 3
Recommendations 0

Share this document

claim authorship

Are you one of the authors of this document?