Cited by 202Additive Manufacturing and 3D Printing TechnologiesAdditive Manufacturing Materials and ProcessesMachine Learning in Materials Science

Machine learning in predicting mechanical behavior of additively manufactured parts

Open access
Nasiri, Sara, Khosravani, Mohammad Reza·2021·Journal of Materials Research and Technology

Abstract

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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Crossref · 10.1016/j.jmrt.2021.07.004

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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].”

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Research works printed but not linked: 1

  1. Xiong J, Zhang G, Hu J, Wu L. Bead geometry prediction for robotic GMAW-based rapidmanufacturing through a neural network and a second-orderregression analysis. J Intell Manuf 2014;25:157e63.

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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
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

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