Machine learning in predicting mechanical behavior of additively manufactured parts
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 g...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-09-01
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| Series: | Journal of Materials Research and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785421006670 |
| _version_ | 1818669792230375424 |
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| author | Sara Nasiri Mohammad Reza Khosravani |
| author_facet | Sara Nasiri Mohammad Reza Khosravani |
| author_sort | Sara Nasiri |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T06:57:50Z |
| format | Article |
| id | doaj.art-67ea4b9608ad42d68db191fa636eacc9 |
| institution | Directory Open Access Journal |
| issn | 2238-7854 |
| language | English |
| last_indexed | 2024-12-17T06:57:50Z |
| publishDate | 2021-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj.art-67ea4b9608ad42d68db191fa636eacc92022-12-21T21:59:21ZengElsevierJournal of Materials Research and Technology2238-78542021-09-011411371153Machine learning in predicting mechanical behavior of additively manufactured partsSara Nasiri0Mohammad Reza Khosravani1Department of Electrical Engineering and Computer Science, University of Siegen, Hoelderlinstr. 3, 57076 Siegen, Germany; SmartyX GmbH (Node 4.0), Martinshardt 19, 57074 Siegen, GermanyChair of Product Development, University of Siegen, Paul-Bonatz-Str. 9-11, 57068 Siegen, Germany; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2238785421006670Mechanical behaviorMachine learning3D printingFracture |
| spellingShingle | Sara Nasiri Mohammad Reza Khosravani Machine learning in predicting mechanical behavior of additively manufactured parts Journal of Materials Research and Technology Mechanical behavior Machine learning 3D printing Fracture |
| title | Machine learning in predicting mechanical behavior of additively manufactured parts |
| title_full | Machine learning in predicting mechanical behavior of additively manufactured parts |
| title_fullStr | Machine learning in predicting mechanical behavior of additively manufactured parts |
| title_full_unstemmed | Machine learning in predicting mechanical behavior of additively manufactured parts |
| title_short | Machine learning in predicting mechanical behavior of additively manufactured parts |
| title_sort | machine learning in predicting mechanical behavior of additively manufactured parts |
| topic | Mechanical behavior Machine learning 3D printing Fracture |
| url | http://www.sciencedirect.com/science/article/pii/S2238785421006670 |
| work_keys_str_mv | AT saranasiri machinelearninginpredictingmechanicalbehaviorofadditivelymanufacturedparts AT mohammadrezakhosravani machinelearninginpredictingmechanicalbehaviorofadditivelymanufacturedparts |