Browse by author
Lookup NU author(s): Dr Eugene WongORCiD, Professor Kenneth Dalgarno
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
Metal Additive Manufacturing (AM) has begun its revolution in various high value industry sectors through enabling design freedom and alleviating laborious machining operations during the production of geometrically complex components. The use of powder bed fusion (PBF) techniques such as Selective Laser Melting (SLM) also promotes material efficiency where unfused granular particles are recyclable after each forming operation in contrast to conventional subtractive methods. However, powder characteristics tend to deviate from their pre-process state following different stages of the process which could affect feedstock behaviour and final part quality. In particular, primary feedstock characteristics including granulometry and morphology must be tightly controlled due to their influence on powder flow and packing behaviour as well as other corresponding attributes which altogether affect material deposition and subsequent laser consolidation. Despite ongoing research efforts which focused strongly on driving process refinement steps to optimise the SLM process, it is also critical to understand the level of material sensitivity towards part forming due to granulometry changes and tackle various reliability as well as quality issues related to powder variation in order to further expand the industrial adoption of the metal additive technique. In this review, the current progress of Metal AM feedstock and various powder characteristics related to the Selective Laser Melting process will be addressed, with a focus on the influence of powder granulometry on feedstock and final part properties.
Author(s): Tan JH, Wong WLE, Dalgarno KW
Publication type: Article
Publication status: Published
Journal: Additive Manufacturing
Year: 2017
Volume: 18
Pages: 228-255
Print publication date: 01/12/2017
Online publication date: 13/10/2017
Acceptance date: 08/10/2017
Date deposited: 16/10/2017
ISSN (print): 2214-8604
ISSN (electronic): 2214-7810
Publisher: Elsevier
URL: https://doi.org/10.1016/j.addma.2017.10.011
DOI: 10.1016/j.addma.2017.10.011
Altmetrics provided by Altmetric
