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This study aims to investigate additive manufacturing of nickel-based superalloy IN718 made by powder bed fusion processes: powder bed fusion laser beam (PBF-LB) and powder bed fusion electron beam (PBF-EB).

This work has focused on the influence of building methods and post-fabrication processes on the final part properties, including microstructure, surface quality, residual stresses and mechanical properties.

PBF-LB produced a much smoother surface. Blasting and shot peening (SP) reduced the roughness even more but did not affect the PBF-EB surface finish as much. As-printed PBF-EB parts have low residual stresses in all directions, whereas it was much higher for PBF-LB. However, heat treatment removed the stresses and SP created compressive stresses for samples from both PBF processes. The standard Arcam process parameter for PBF-EB for IN718 is not fully optimized, which leads to porosity and inferior mechanical properties. However, impact toughness after hot isostatic pressing was surprisingly high.

The two processes gave different results and also responses to post-treatments, which could be of advantage or disadvantage for different applications. Suggestions for improving the properties of parts produced by each method are presented.

This paper aims to present a methodology for the detection and categorisation of metal powder particles that are partially attached to additively manufactured lattice structures. It proposes a software algorithm to process micro computed tomography (µCT) image data, thereby providing a systematic and formal basis for the design and certification of powder bed fusion lattice structures, as is required for the certification of medical implants.

This paper details the design and development of a software algorithm for the analysis of µCT image data. The algorithm was designed to allow statistical probability of results based on key independent variables. Three data sets with a single unique parameter were input through the algorithm to allow for characterisation and analysis of like data sets.

This paper demonstrates the application of the proposed algorithm with three data sets, presenting a detailed visual rendering derived from the input image data, with the partially attached particles highlighted. Histograms for various geometric attributes are output, and a continuous trend between the three different data sets is highlighted based on the single unique parameter.

This paper presents a novel methodology for non-destructive algorithmic detection and categorisation of partially attached metal powder particles, of which no formal methods exist. This material is available to download as a part of a provided GitHub repository.