AccScience Publishing / ESAM / Online First / DOI: 10.36922/ESAM025280018
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ORIGINAL RESEARCH ARTICLE

A machine learning approach for enhancing process screening and qualification in metal additive manufacturing

Ze Chen1,2 ,  Jingwen Gao2 ,  Chengcheng Wang2 ,  Zhuohong Zeng1,2 ,  Chenyang Zhu2 ,  Wei Fan1,3*
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1 Singapore Centre for 3D Printing, Nanyang Technological University, Singapore
2 School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
3 State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi’an, Shaanxi, China
ESAM, 025280018 https://doi.org/10.36922/ESAM025280018
Received: 9 July 2025 | Revised: 14 August 2025 | Accepted: 14 August 2025 | Published online: 29 August 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

The prevailing screening and qualification methodologies heavily depend on conventional manufacturing processes, which incur significant costs and prolonged lead times due to extensive physical testing. These challenges are also present in the growing field of additive manufacturing (AM), where numerous process parameters must be considered. However, the net-shape forming advantage of AM renders conventional screening and qualification methods inadequate. In the context of ongoing industrial digital transformation, a promising approach to enhancing process screening and qualification for metal AM is the adoption of a digital methodology tailored to the unique characteristics of this manufacturing technique. In this study, a convolutional neural network model is employed to extract features from images to predict material properties in laser-directed energy deposition (L-DED) processes. The model achieved a mean absolute percentage error of 2.3% and a root mean square error of 15.0 MPa for predicting ultimate tensile strength, with a prediction residual within ±1% for density. Unlike conventional approaches that rely on bulk or multilayer builds, this study uniquely demonstrates the feasibility of using early-stage single-track print features to predict final part properties with limited view and material involvement. This established model and workflow pave the way for highly efficient and low-cost property prediction in L-DED processes.

Graphical abstract
Keywords
Additive manufacturing
Directed energy deposition
Machine learning
Process screening
Qualification
Funding
This work was supported by the Fundamental Research Funds for the Central Universities (Grant No. D5000250077).
Conflict of interest
Wei Fan is an Editorial Board Member of this journal, but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
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Engineering Science in Additive Manufacturing, Electronic ISSN: 3082-849X Published by AccScience Publishing
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