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Research Article | Open Access

Generating synthetic as-built additive manufacturing surface topography using progressive growing generative adversarial networks

Junhyeon SEO1, Prahalada RAO2, Bart RAEYMAEKERS1( )
Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24061, USA
Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USA
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Graphical Abstract

Abstract

Numerically generating synthetic surface topography that closely resembles the features and characteristics of experimental surface topography measurements reduces the need to perform these intricate and costly measurements. However, existing algorithms to numerically generated surface topography are not well-suited to create the specific characteristics and geometric features of as-built surfaces that result from laser powder bed fusion (LPBF), such as partially melted metal particles, porosity, laser scan lines, and balling. Thus, we present a method to generate synthetic as-built LPBF surface topography maps using a progressively growing generative adversarial network. We qualitatively and quantitatively demonstrate good agreement between synthetic and experimental as-built LPBF surface topography maps using areal and deterministic surface topography parameters, radially averaged power spectral density, and material ratio curves. The ability to accurately generate synthetic as-built LPBF surface topography maps reduces the experimental burden of performing a large number of surface topography measurements. Furthermore, it facilitates combining experimental measurements with synthetic surface topography maps to create large data-sets that facilitate, e.g. relating as-built surface topography to LPBF process parameters, or implementing digital surface twins to monitor complex end-use LPBF parts, amongst other applications.

Keywords

additive manufacturingsurface topographysynthetic surface topographygenerative adversarial networks

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Friction
Volume 12 Issue 6,
June 2024
Pages 1283-1298
DOI: 10.1007/s40544-023-0826-7
Cite this article:
SEO J, RAO P, RAEYMAEKERS B. Generating synthetic as-built additive manufacturing surface topography using progressive growing generative adversarial networks. Friction, 2024, 12(6): 1283-1298. https://doi.org/10.1007/s40544-023-0826-7

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Received: 28 June 2023
Revised: 25 August 2023
Accepted: 15 September 2023
Published: 04 December 2023
© The author(s) 2023.

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