A model for the rapid prediction of small feature width producible by Metal AM

Sibanda, Prospera Sonuo, Ryan, Michael ORCID: https://orcid.org/0000-0002-8104-0121 and Bigot, Samuel ORCID: https://orcid.org/0000-0002-0789-4727 2024. A model for the rapid prediction of small feature width producible by Metal AM. Presented at: 17th CIRP Conference on Intelligent Computation in Manufacturing Engineering, Gulf of Naples, Italy, 12-14 July 2023. Procedia CIRP. , vol.126 Elsevier, pp. 597-602. 10.1016/j.procir.2024.08.246

PDF - Accepted Post-Print Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (566kB)

Abstract

In this paper, an analytical parametric model is presented for the swift prediction of small features producible in the X-Y plane through the Metal AM Laser Powder Bed Fusion (LPBF) process. This model can be employed to design and create surface textures on Additive Manufactured parts without the need for costly post-processing steps. The Rosenthal equation is the basis for the model, which considers both the build parameters of the LPBF process and the thermo-physical properties of the materials. The initial model was constructed and assessed using one LPBF machine followed by the implementation of a tuning method utilizing the Limited Memory Algorithm for Bound Constrained Optimization to enhance the model’s accuracy. Overall, the findings suggest that with a simple optimization step based on a single printed tuning sample, precise analytical models can be established for specific LPBF machines and materials combinations.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	2212-8271
Date of First Compliant Deposit:	6 September 2024
Last Modified:	16 Oct 2024 15:16
URI:	https://orca.cardiff.ac.uk/id/eprint/171908

Actions (repository staff only)

Edit Item