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Developments in PBF-LB
Laser Beam Powder Bed Fusion: Process developments and numerical simulation A technical session at the Euro PM2021 Virtual Congress, organised by the European Powder Metallurgy Association (EPMA) and held October 18–22, 2021, was devoted to the consideration of process developments and numerical simulation approaches for Powder Bed Fusion (PBF) Additive Manufacturing technologies. In this report, Dr David Whittaker reviews four of the papers presented on this topic, looking at process parameter optimisation, increasing quality for Ti6Al4V medical parts, techniques to improve the AM of hot-work tool steels, and powder spreading improvements for stainless steel.
Efficient process parameter optimisation procedure in Laser Beam Powder Bed Fusion The first of the papers in this session came from Maria Montero-Sistiaga, Marc De Smit, Ralph Haagsma and Ian Bennett (NLR, the Netherlands), and showcased this group’s development of an efficient process parameter optimisation procedure for Laser Beam Powder Bed Fusion (PBF-LB) [1]. Important PBF-LB material properties, such as porosity, microstructure and surface roughness, are largely determined by the applied layer thickness, laser power, scan velocity and distance between laser scan vectors. Past studies aimed at PBF-LB parameter optimisation have generally been based on the definition
of a test matrix that covers an array of samples with different combinations of parameters. These methods require the production and analysis of a large number of samples, making them time consuming and expensive. This presented paper described work done on the development of a new methodology for selecting PBF-LB parameters. The approach was based on the analysis of a large number of parameter combinations with a minimum number of samples. Parameters were optimised for processing AlSi10Mg alloy, which was selected because of its suitability for use in thermal control applications. In this work, the methodology for optimising the contour, hatch (also known as core or bulk) and interface parameters was investigated. For optimisation of the contour, thin walls were built with varying laser power and scan speed. For the
hatch area, blocks were built with varying hatch distance along the sample length. Finally, for selection of the optimum interface settings, the hatch area was rotated relative to the sample contour in order to induce a variable offset between the contours and the hatch. Parameter selection was carried out based on analysis of sample cross-sections. The selection methodology was described for each optimised setting. The AlSi10Mg powder used in the study had the composition shown in Table 1. The powder particles were not fully spherical and had sizes between 20–63 μm, with an average size of 43.8 μm. The samples were processed on an SLM 280HL machine. Three types of geometry were generated: thin walls, blocks with varying hatch distance and blocks with rotated hatch, as shown in Fig. 1.
Al
Si
Mg
Fe
O
Ni
Zn
Ti
Pb
Mn
Cu
Other
Bal.
10.10
0.36
0.22
0.06
<0.01
<0.1
<0.01
<0.01
<0.01
<0.05
<0.05
Table 1 Composition of AlSi10Mg powder in weight percent [1]
Vol. 7 No. 4 © 2021 Inovar Communications Ltd
Metal Additive Manufacturing | Winter 2021
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