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Dokument Type: Article
metadata.dc.title: Machine learning in predicting mechanical behavior of additively manufactured parts
Authors: Nasiri, Sara 
Khosravani, Mohammad Reza 
Institute: Fakultät IV - Naturwissenschaftlich-Technische Fakultät 
Free keywords: Desktop Manufacturing, Layer Manufacturing, Additive Manufacturing, 3D Print
Dewey Decimal Classification: 670 Industrielle und handwerkliche Fertigung
GHBS-Clases: ZHU
Issue Date: 2021
Publish Date: 2022
Source: Journal of Materials Research and Technology ; 14(2021), S. 1137-1153. -
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 3Dprinted
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.
Finanziert aus dem Open-Access-Publikationsfonds der Universität Siegen für Zeitschriftenartikel
URN: urn:nbn:de:hbz:467-21324
Appears in Collections:Geförderte Open-Access-Publikationen

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