Melt pool monitoring and x-ray computed tomography-informed characterisation of laser powder bed additively manufactured silver–diamond composites
Abstract
In this study, silver (Ag) and silver–diamond (Ag-D) composites with varying diamond (D) content are fabricated using laser powder bed fusion (L-PBF) additive manufacturing (AM). The L-PBF process parameters and inert gas flow rate are optimised to control the build environment and the laser energy density at the powder bed to enable the manufacture of Ag-D composites with 0.1%, 0.2% and 0.3% D content. The Ag and D powder morphology are characterised using scanning electron microscopy (SEM). Ag, Ag-D0.1%, Ag-D0.2% and Ag-D0.3% tensile samples are manufactured to assess the resultant density and tensile strength. In-process EOSTATE melt pool monitoring technology is utilised as a comparative tool to assess the density variations. This technique uses in-process melt pool detection to identify variations in the melt pool characteristics and potential defects and/or density deviations. The resultant morphology and associated defect distribution for each of the samples are characterised and reported using X-ray computed tomography (xCT) and 3D visualisation techniques. Young’s modulus, the failure strain and the ultimate tensile strength of the L-PBF Ag and Ag-D are reported. The melt pool monitoring results revealed in-process variations in the build direction, which was confirmed through xCT 3D visualisations. Additionally, the xCT analysis displayed density variations for all the Ag-D composites manufactured. The tensile results revealed that increasing the diamond content reduced Young’s modulus and the ultimate tensile strength.Citation
Robinson J, Arafat A, Vance A, Arjunan A, Baroutaji A. (2023) Melt Pool Monitoring and X-ray Computed Tomography-Informed Characterisation of Laser Powder Bed Additively Manufactured Silver–Diamond Composites. Machines, 11(12):1037. https://doi.org/10.3390/machines11121037Publisher
MDPIJournal
MachinesAdditional Links
https://www.mdpi.com/2075-1702/11/12/1037Type
Journal articleLanguage
enDescription
© 2023 The Authors. Published by MDPI. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3390/machines11121037ISSN
2075-1702EISSN
2075-1702Sponsors
Innovate UK Knowledge Transfer Partnership KTP013117 (University of Wolverhampton/AceOn).ae974a485f413a2113503eed53cd6c53
10.3390/machines11121037
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