Physics‐based modeling and predictive simulation of powder bed fusion additive manufacturing across length scales
Powder bed fusion additive manufacturing (PBFAM) of metals has the potential to enable new paradigms of product design, manufacturing and supply chains while accelerating the realization of new technologies in the medical, aerospace, and other industries. Currently, wider adoption of PBFAM is held b...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023
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| Online Access: | https://hdl.handle.net/1721.1/153096 |
| _version_ | 1826209292014845952 |
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| author | Meier, Christoph Fuchs, Sebastian L. Much, Nils Nitzler, Jonas Penny, Ryan W. Praegla, Patrick M. Proell, Sebastian D. Sun, Yushen Weissbach, Reimar Schreter, Magdalena Hodge, Neil E. John Hart, A. Wall, Wolfgang A. |
| author_facet | Meier, Christoph Fuchs, Sebastian L. Much, Nils Nitzler, Jonas Penny, Ryan W. Praegla, Patrick M. Proell, Sebastian D. Sun, Yushen Weissbach, Reimar Schreter, Magdalena Hodge, Neil E. John Hart, A. Wall, Wolfgang A. |
| author_sort | Meier, Christoph |
| collection | MIT |
| description | Powder bed fusion additive manufacturing (PBFAM) of metals has the potential to enable new paradigms of product design, manufacturing and supply chains while accelerating the realization of new technologies in the medical, aerospace, and other industries. Currently, wider adoption of PBFAM is held back by difficulty in part qualification, high production costs and low production rates, as extensive process tuning, post‐processing, and inspection are required before a final part can be produced and deployed. Physics‐based modeling and predictive simulation of PBFAM offers the potential to advance fundamental understanding of physical mechanisms that initiate process instabilities and cause defects. In turn, these insights can help link process and feedstock parameters with resulting part and material properties, thereby predicting optimal processing conditions and inspiring the development of improved processing hardware, strategies and materials. This work presents recent developments of our research team in the modeling of metal PBFAM processes spanning length scales, namely mesoscale powder modeling, mesoscale melt pool modeling, macroscale thermo‐solid‐mechanical modeling and microstructure modeling. Ongoing work in experimental validation of these models is also summarized. In conclusion, we discuss the interplay of these individual submodels within an integrated overall modeling approach, along with future research directions. |
| first_indexed | 2024-09-23T14:20:13Z |
| format | Article |
| id | mit-1721.1/153096 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:20:13Z |
| publishDate | 2023 |
| publisher | Wiley |
| record_format | dspace |
| spelling | mit-1721.1/1530962023-12-01T03:17:17Z Physics‐based modeling and predictive simulation of powder bed fusion additive manufacturing across length scales Meier, Christoph Fuchs, Sebastian L. Much, Nils Nitzler, Jonas Penny, Ryan W. Praegla, Patrick M. Proell, Sebastian D. Sun, Yushen Weissbach, Reimar Schreter, Magdalena Hodge, Neil E. John Hart, A. Wall, Wolfgang A. Powder bed fusion additive manufacturing (PBFAM) of metals has the potential to enable new paradigms of product design, manufacturing and supply chains while accelerating the realization of new technologies in the medical, aerospace, and other industries. Currently, wider adoption of PBFAM is held back by difficulty in part qualification, high production costs and low production rates, as extensive process tuning, post‐processing, and inspection are required before a final part can be produced and deployed. Physics‐based modeling and predictive simulation of PBFAM offers the potential to advance fundamental understanding of physical mechanisms that initiate process instabilities and cause defects. In turn, these insights can help link process and feedstock parameters with resulting part and material properties, thereby predicting optimal processing conditions and inspiring the development of improved processing hardware, strategies and materials. This work presents recent developments of our research team in the modeling of metal PBFAM processes spanning length scales, namely mesoscale powder modeling, mesoscale melt pool modeling, macroscale thermo‐solid‐mechanical modeling and microstructure modeling. Ongoing work in experimental validation of these models is also summarized. In conclusion, we discuss the interplay of these individual submodels within an integrated overall modeling approach, along with future research directions. 2023-11-30T21:35:45Z 2023-11-30T21:35:45Z 2021-08-22 2023-11-30T21:24:05Z Article http://purl.org/eprint/type/JournalArticle 0936-7195 https://hdl.handle.net/1721.1/153096 Meier, Christoph, Fuchs, Sebastian L., Much, Nils, Nitzler, Jonas, Penny, Ryan W. et al. 2021. "Physics‐based modeling and predictive simulation of powder bed fusion additive manufacturing across length scales." GAMM-Mitteilungen, 44 (3). en 10.1002/gamm.202100014 GAMM-Mitteilungen Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf Wiley Wiley-VCH |
| spellingShingle | Meier, Christoph Fuchs, Sebastian L. Much, Nils Nitzler, Jonas Penny, Ryan W. Praegla, Patrick M. Proell, Sebastian D. Sun, Yushen Weissbach, Reimar Schreter, Magdalena Hodge, Neil E. John Hart, A. Wall, Wolfgang A. Physics‐based modeling and predictive simulation of powder bed fusion additive manufacturing across length scales |
| title | Physics‐based modeling and predictive simulation of powder bed fusion additive manufacturing across length scales |
| title_full | Physics‐based modeling and predictive simulation of powder bed fusion additive manufacturing across length scales |
| title_fullStr | Physics‐based modeling and predictive simulation of powder bed fusion additive manufacturing across length scales |
| title_full_unstemmed | Physics‐based modeling and predictive simulation of powder bed fusion additive manufacturing across length scales |
| title_short | Physics‐based modeling and predictive simulation of powder bed fusion additive manufacturing across length scales |
| title_sort | physics based modeling and predictive simulation of powder bed fusion additive manufacturing across length scales |
| url | https://hdl.handle.net/1721.1/153096 |
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