Classification of defects in additively manufactured nickel alloys using supervised machine learning

Abstract

The presence of undesirable microstructural features in additively manufactured components, such as cracks, pores and lack of fusion defects presents a challenge for engineers, particularly if these components are applied in structure-critical applications. Such features might need to be manually classified, counted and their size distributions measured during metallographic evaluation, which is a time-consuming task. In this study, the performance of two supervised machine learning methods (kth-nearest neighbours and decision trees) to automatically classify typical defects found during metallographic examination of additively manufactured nickel alloys is briefly outlined and discussed.

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Item Type:	Article
Authors/Creators:	Aziz, U. Bradshaw, A. Lim, J. Thomas, M. https://orcid.org/0000-0002-9804-3775
Copyright, Publisher and Additional Information:	© 2023 The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. Request permissions for this article.
Keywords:	Additive manufacturing; nickel alloys; defects; machine learning
Dates:	Accepted: 20 April 2023 Published (online): 1 November 2023 Published: November 2023
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Engineering (Sheffield) > School of Chemical, Materials and Biological Engineering
Depositing User:	Symplectic Sheffield
Date Deposited:	22 Sep 2025 09:47
Last Modified:	22 Sep 2025 09:47
Status:	Published
Publisher:	SAGE Publications
Refereed:	Yes
Identification Number:	10.1080/02670836.2023.2207337
Related URLs:	Author
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:231983

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Classification of defects in additively manufactured nickel alloys using supervised machine learning

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