In‐Situ Rheology Measurements via Machine‐Learning Enhanced Direct‐Ink‐Writing
Direct ink writing, an extrusion‐based 3D printing method, is well suited for high‐mix low‐volume manufacturing. However, an iterative approach, using random selection or constant expert guidance, is still used to create printable inks and optimize printing parameters by expending significant amount...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Advanced Intelligent Systems |
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| Online Access: | https://doi.org/10.1002/aisy.202400293 |
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| author | Robert D. Weeks Jennifer M. Ruddock J. Daniel Berrigan Jennifer A. Lewis James. O. Hardin |
| author_facet | Robert D. Weeks Jennifer M. Ruddock J. Daniel Berrigan Jennifer A. Lewis James. O. Hardin |
| author_sort | Robert D. Weeks |
| collection | DOAJ |
| description | Direct ink writing, an extrusion‐based 3D printing method, is well suited for high‐mix low‐volume manufacturing. However, an iterative approach, using random selection or constant expert guidance, is still used to create printable inks and optimize printing parameters by expending significant amounts of time, materials, and effort. Herein, a machine learning (ML) model that estimates ink rheology in‐situ from a simple printed test pattern is reported. This ML model is trained with a rheologically diverse set of inks composed of different polymers. The model successfully correlated features of the simple printed test pattern to rheological properties, which could, in theory, inform both printed structures and future ink compositions. The behavior of this model is verified and analyzed with explainable artificial intelligence tools, linking printed feature importance to one's known physical understanding of the process. |
| format | Article |
| id | doaj-art-32698be492e449e5afbb7c15e7d3b198 |
| institution | Kabale University |
| issn | 2640-4567 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-32698be492e449e5afbb7c15e7d3b1982025-01-21T07:26:27ZengWileyAdvanced Intelligent Systems2640-45672025-01-0171n/an/a10.1002/aisy.202400293In‐Situ Rheology Measurements via Machine‐Learning Enhanced Direct‐Ink‐WritingRobert D. Weeks0Jennifer M. Ruddock1J. Daniel Berrigan2Jennifer A. Lewis3James. O. Hardin4John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USAUES Inc. 4401 Dayton‐Xenia Rd Dayton OH 45432 USAMaterials and Manufacturing Directorate Air Force Research Laboratory 2977 Hobson Way Wright‐Patterson AFB, OH 45433‐7126 USAJohn A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USAMaterials and Manufacturing Directorate Air Force Research Laboratory 2977 Hobson Way Wright‐Patterson AFB, OH 45433‐7126 USADirect ink writing, an extrusion‐based 3D printing method, is well suited for high‐mix low‐volume manufacturing. However, an iterative approach, using random selection or constant expert guidance, is still used to create printable inks and optimize printing parameters by expending significant amounts of time, materials, and effort. Herein, a machine learning (ML) model that estimates ink rheology in‐situ from a simple printed test pattern is reported. This ML model is trained with a rheologically diverse set of inks composed of different polymers. The model successfully correlated features of the simple printed test pattern to rheological properties, which could, in theory, inform both printed structures and future ink compositions. The behavior of this model is verified and analyzed with explainable artificial intelligence tools, linking printed feature importance to one's known physical understanding of the process.https://doi.org/10.1002/aisy.2024002933D printingartificial intelligenceexplainable artificial intelligencemachine learningrheology |
| spellingShingle | Robert D. Weeks Jennifer M. Ruddock J. Daniel Berrigan Jennifer A. Lewis James. O. Hardin In‐Situ Rheology Measurements via Machine‐Learning Enhanced Direct‐Ink‐Writing Advanced Intelligent Systems 3D printing artificial intelligence explainable artificial intelligence machine learning rheology |
| title | In‐Situ Rheology Measurements via Machine‐Learning Enhanced Direct‐Ink‐Writing |
| title_full | In‐Situ Rheology Measurements via Machine‐Learning Enhanced Direct‐Ink‐Writing |
| title_fullStr | In‐Situ Rheology Measurements via Machine‐Learning Enhanced Direct‐Ink‐Writing |
| title_full_unstemmed | In‐Situ Rheology Measurements via Machine‐Learning Enhanced Direct‐Ink‐Writing |
| title_short | In‐Situ Rheology Measurements via Machine‐Learning Enhanced Direct‐Ink‐Writing |
| title_sort | in situ rheology measurements via machine learning enhanced direct ink writing |
| topic | 3D printing artificial intelligence explainable artificial intelligence machine learning rheology |
| url | https://doi.org/10.1002/aisy.202400293 |
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