Critical statistical assessment of data in metal additive manufacturing
File(s)
Published version
Author(s)
Wong, Raymond
Tran, Anh
Dovgyy, Bogdan
Santos Maldonado, Claudia
Pham, Minh-Son
Type
Journal Article
Abstract
Obtaining high quality data reflecting the relationships between the additive manufacturing (AM) process parameters, material microstructure and mechanical properties is crucial for the use of machine learning in AM. A database of over 4,000 data entries of metal AM was created thanks to a large number of literature studies on key process parameters and indicators of build quality. Meta-analysis reveals critical biases in the literature. Firstly, majority of studies report only high quality builds, these imbalances in reporting result in weak correlation between process parameters, properties and consolidation, limiting the ability of machine learning models to generalize beyond optimized conditions. Nevertheless, the trained models accurately predict yield strength (R² = 0.85), suggesting that certain process–property relationships are effectively captured within these models. Secondly, quantitative microstructural data are largely absent, limiting the learning of the microstructure-mechanical properties relationships. Finally, current process window identification is based largely on the consolidation, despite significant uncertainty in its measurement. It is important to identify the process map on the basis of not only the consolidation, but also mechanical behavior under loading. Such a identification shows that 316L and Inconel have much larger process map (i.e. highly printable) in comparison to the AlSi10Mg and Ti6Al4V.
Date Issued
2025-08-01
Date Acceptance
2025-06-23
Citation
Materials and Design, 2025, 256
ISSN
0264-1275
Publisher
Elsevier
Journal / Book Title
Materials and Design
Volume
256
Copyright Statement
2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
10.1016/j.matdes.2025.114301
Publication Status
Published
Article Number
ARTN 114301
Date Publish Online
2025-07-04