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Discrete element simulation of powder layer thickness in laser additive manufacturing

Han, Quanquan, Gu, Heng and Setchi, Rossitza 2019. Discrete element simulation of powder layer thickness in laser additive manufacturing. Powder Technology 352 , pp. 91-102. 10.1016/j.powtec.2019.04.057
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Abstract

The optimisation of the laser additive manufacturing (AM) process is a challenging task when a new material is considered. Compared to the selection of other process parameters such as laser power, scanning speed and hatch spacing, the optimisation of powder layer thickness is much more time-consuming and costly because a new run is normally needed when the layer thickness value is changed. In practice, the layer thickness is fixed to a value that is slightly higher than the average particle size. This paper introduces a systematic approach to layer thickness optimisation based on a theoretical model of the interactions between the particles, the wiper and the build plate during the powder deposition. The focus is on a systematic theoretical and experimental investigation of the effect of powder layer thickness on various powder bed characteristics during single-layer and multi-layer powder deposition. The theoretical model was tested experimentally using Hastelloy X (HX) with an average particle size of 34.4 μm. The experimental results validated the simulation model, which predicted a uniform powder bed deposition when employing a 40 μm layer thickness value. Lower (30 μm) and higher (50 μm) layer thickness values resulted in large voids and short-feed defects, respectively. The subsequent optimisation of the scanning speed and hatch spacing parameters was executed using a 40 μm layer thickness. The optimum process parameters were then used to examine the microstructure and tensile performance of the as-fabricated HX. This study provides an improved understanding of the powder deposition process and offers insights into the selection of suitable powder layer thicknesses in laser AM.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Elsevier
ISSN: 0032-5910
Date of First Compliant Deposit: 2 May 2019
Date of Acceptance: 23 April 2019
Last Modified: 20 May 2019 08:59
URI: http://orca.cf.ac.uk/id/eprint/122113

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