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Characterisation methods for powder bed fusion processed surface topography

Lou, S.; Jiang, X.; Sun, W.; Zeng, W.; Pagani, L.; Scott, P.J. (2019) Characterisation methods for powder bed fusion processed surface topography. Precision Engineering, 57. pp. 1-15. ISSN 01416359

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Abstract

Powder bed fusion (PBF) is a popular additive manufacturing (AM) process with wide applications in key industrial sectors, including aerospace, automotive, healthcare, defence, etc. However, a deficiency of PBF is its low quality of surface finish. A number of PBF process variables and other factors (e.g. powders, recoater) can influence the surface quality. It is of significant importance to measure and characterise PBF surfaces for the benefits of process optimisation, product performance evaluation and also product design. A literature review is given to summarise the current research work concerned with the measurement and characterisation of AM surfaces, particularly PBF surfaces. It is recognised that AM processes are different from conventional manufacturing processes and their produced surface topographies are different as well. In this paper, the surface characterisation framework is updated to reflect the unique characters of PBF processes. The surface spatial wavelength components and other process signature features are described and their production mechanisms are elaborated. A specific surface characterisation procedure is developed based on the updated framework. The robust Gaussian filter and the morphological filters are proposed to be used for the separation of the waviness component due to their robustness. The watershed segmentation is enhanced to extract globules from the residual surface. Two examples of surfaces produced by two common PBF processes, electron beam melting (EBM) and selective laser melting (SLM), are used to illustrate the application of these techniques for the characterisation of PBF surface topography.

Item Type: Article
Subjects: Engineering Measurements > Dimensional
Divisions: Engineering
Identification number/DOI: 10.1016/j.precisioneng.2018.09.007
Last Modified: 26 May 2020 14:47
URI: http://eprintspublications.npl.co.uk/id/eprint/8680

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