Lopez-Botello, O, Martinez Hernandez, U, Ramírez, J et al. (2 more authors) (2017) Two-dimensional simulation of grain structure growth within selective laser melted AA-2024. Materials and Design, 113. pp. 369-376. ISSN 0261-3069
Abstract
A two-dimensional Cellular Automata (CA) – Finite Element (FE) (CA-FE) coupled model has been developed to predict the microstructures formed during the laser melting of a powdered AA-2024 feedstock using the Additive Manufacturing (AM) process Selective Laser Melting (SLM). The presented CA model is coupled with a thermal FE model, which computes the heat flow characteristics of the SLM process. The developed model considers the powder-to-liquid-to-solid transformation, tracks the interaction between several melt pools within a melted track, and several tracks within various layers. It was found that the calculated temperature profiles as well as the simulated microstructures bared close resemblance with SLM fabricated AA-2024 samples. The developed model was capable of predicting melt pool cooling and solidification rates, the type of microstructure obtained, the size of the melt pool (with 14% error) and the heat affected zone, average grain size number (with 12% error) and the growth competition present in microstructures of components manufactured via SLM.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Grain structure; Cellular Automata; Finite Element; Selective Laser Melting; Additive Manufacturing |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Systems and Design (iESD) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 12 Dec 2016 12:14 |
Last Modified: | 23 Jun 2023 22:19 |
Published Version: | https://doi.org/10.1016/j.matdes.2016.10.031 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.matdes.2016.10.031 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:109356 |