Machine learning in predicting mechanical behavior of additively manufactured parts
Open accessAbstract
Although applications of additive manufacturing (AM) have been significantly increased in recent years, its broad application in several industries is still under progress. AM also known as three-dimensional (3D) printing is layer by layer manufacturing process which can be used for fabrication of geometrically complex customized functional end-use products. Since AM processing parameters have significant effects on the performance of the printed parts, it is necessary to tune these parameters which is a difficult task. Today, different artificial intelligence techniques have been utilized to optimize AM parameters and predict mechanical behavior of 3D-printed components. In the present study, applications of machine learning (ML) in prediction of structural performance and fracture of additively manufactured components has been presented. This study first outlines an overview of ML and then summarizes its applications in AM. The main part of this review, focuses on applications of ML in prediction of mechanical behavior and fracture of 3D-printed parts. To this aim, previous research works which investigated application of ML in characterization of polymeric and metallic 3D-printed parts have been reviewed and discussed. Moreover, the review and analysis indicate limitations, challenges, and perspectives for industrial applications of ML in the field of AM. Considering advantages of ML increase in applications of ML in optimization of 3D printing parameters, prediction of mechanical performance, and evaluation of 3D-printed products is expected.
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Machine learning models can be developed to predict mechanical properties of materials fabricated through additive manufacturing as they can learn and model highly complex relationships.
“Machine learning models can be developed to predict mechanical properties of materials fabricated through additive manufacturing as they can learn and model highly complex relationships [438-445].”
From A comprehensive review of lunar-based manufacturing and construction (2024) · cites this paper as [445]
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Flagged first, then by citations
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- Authors
- Nasiri, S., & Khosravani, M. R.
- Affiliations
- University of Siegen (DE)
- Corresponding author
- Khosravani, Mohammad Reza
- Venue
- Journal of Materials Research and Technology
- Publisher
- Elsevier BV
- Published
- 2021-09
- Volume
- 14
- Pages
- 1137-1153
- ISSN
- 2214-0697 (ISSN-L)
- 2238-7854
- OpenAlex
- W3182644282
- Cited by
- 202
- References
- 133
- Citations by year
- 2026: 29 · 2025: 43 · 2024: 34 · 2023: 41 · 2022: 44 · 2021: 11
- FWCI
- 10.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Materials Research and Technology (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Recherche und Kataloge (Universitätsbibliothek Siegen)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Additive Manufacturing and 3D Printing Technologies
- Physical Sciences › Engineering › Mechanical Engineering › Additive Manufacturing Materials and Processes
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- additive manufacturing, 3D printing, machine learning, mechanical behavior prediction, fracture prediction, printing parameter optimization, structural performance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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