A robust methodology for surface roughness characterisation of additive manufactured parts

Mason, Benjamin ORCID: https://orcid.org/0000-0002-2136-3252, Kundu, Abhishek ORCID: https://orcid.org/0000-0002-8714-4087, Ryan, Michael ORCID: https://orcid.org/0000-0002-8104-0121, Setchi, Rossitza ORCID: https://orcid.org/0000-0002-7207-6544 and Bhaduri, Debajyoti ORCID: https://orcid.org/0000-0002-8270-388X 2025. A robust methodology for surface roughness characterisation of additive manufactured parts. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications 10.1177/1464420725135381

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Abstract

Additively manufactured (AM) parts typically possess high surface roughness (∼5–30 μm) and large surface features, resulting from balling, partial sintering/melting, and staircase effects. However, there are few widely accepted/adopted methodologies for measuring and characterising AM surfaces. This research proposes a practical, reliable, and repeatable methodology for the measurement, analysis and characterisation of surfaces produced via AM. Various line and surface roughness parameters are measured on the top and side faces of AM cubes, using both tactile and optical profilometers. The line-based tactile roughness measurement approach is considerably faster than surface measurements but offers a limited range of accuracy. The study has undertaken an exhaustive analysis to establish the applicability and degree of accuracy of line-measured roughness metrics, when benchmarked with full-surface measurements. An analysis of the limited range of applicability of line measurements is provided within a framework of probabilistic uncertainty analysis, performed on measured roughness data. This study also explores alternative methods for reducing the measurement time required without compromising the reliability of the results, by decreasing the resolution of the scanned data. It is demonstrated that deviations in surface parameters remained within 2% when the resolution was reduced by 50%; consequently, the measurement time was reduced by ∼75%.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Engineering
Publisher:	SAGE Publications
ISSN:	1464-4207
Date of First Compliant Deposit:	14 July 2025
Date of Acceptance:	13 June 2025
Last Modified:	16 Jul 2025 13:15
URI:	https://orca.cardiff.ac.uk/id/eprint/179818

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