Predicting Properties of Fused Filament Fabrication Parts through Sensors and Machine Learning
Fused filament fabrication (FFF), colloquially known as 3D-printing, has gradually expanded from the laboratory to the industrial and household realms due to its suitability for producing highly customized products with complex geometries. However, it is difficult to evaluate the mechanical performa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Journal of Manufacturing and Materials Processing |
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| Online Access: | https://www.mdpi.com/2504-4494/7/5/186 |
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| author | Zijie Liu Gerardo A. Mazzei Capote Evan Grubis Apoorv Pandey Juan C. Blanco Campos Graydon R. Hegge Tim A. Osswald |
| author_facet | Zijie Liu Gerardo A. Mazzei Capote Evan Grubis Apoorv Pandey Juan C. Blanco Campos Graydon R. Hegge Tim A. Osswald |
| author_sort | Zijie Liu |
| collection | DOAJ |
| description | Fused filament fabrication (FFF), colloquially known as 3D-printing, has gradually expanded from the laboratory to the industrial and household realms due to its suitability for producing highly customized products with complex geometries. However, it is difficult to evaluate the mechanical performance of samples produced by this method of additive manufacturing (AM) due to the high number of combinations of printing parameters, which have been shown to significantly impact the final structural integrity of the part. This implies that using experimental data attained through destructive testing is not always viable. In this study, predictive models based on the rapid prediction of the required extrusion force and mechanical properties of printed parts are proposed, selecting a subset of the most representative printing parameters during the printing process as the domain of interest. Data obtained from the in-line sensor-equipped 3D printers were used to train several different predictive models. By comparing the coefficient of determination (R<sup>2</sup>) of the response surface method (RSM) and five different machine learning models, it is found that the support vector regressor (SVR) has the best performance in this data volume case. Ultimately, the ML resources developed in this work can potentially support the application of AM technology in the assessment of part structural integrity through simulation and can also be integrated into a control loop that can pause or even correct a failing print if the expected filament force-speed pairing is trailing outside a tolerance zone stemming from ML predictions. |
| first_indexed | 2024-03-10T21:09:27Z |
| format | Article |
| id | doaj.art-ff542b6f37d94b8faedcf20b7e805454 |
| institution | Directory Open Access Journal |
| issn | 2504-4494 |
| language | English |
| last_indexed | 2024-03-10T21:09:27Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Manufacturing and Materials Processing |
| spelling | doaj.art-ff542b6f37d94b8faedcf20b7e8054542023-11-19T16:57:25ZengMDPI AGJournal of Manufacturing and Materials Processing2504-44942023-10-017518610.3390/jmmp7050186Predicting Properties of Fused Filament Fabrication Parts through Sensors and Machine LearningZijie Liu0Gerardo A. Mazzei Capote1Evan Grubis2Apoorv Pandey3Juan C. Blanco Campos4Graydon R. Hegge5Tim A. Osswald6Polymer Engineering Center, Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706-1691, USAPolymer Engineering Center, Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706-1691, USAPolymer Engineering Center, Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706-1691, USAPolymer Engineering Center, Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706-1691, USAFused Form Corp., Bogotá 110111, ColombiaPolymer Engineering Center, Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706-1691, USAPolymer Engineering Center, Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706-1691, USAFused filament fabrication (FFF), colloquially known as 3D-printing, has gradually expanded from the laboratory to the industrial and household realms due to its suitability for producing highly customized products with complex geometries. However, it is difficult to evaluate the mechanical performance of samples produced by this method of additive manufacturing (AM) due to the high number of combinations of printing parameters, which have been shown to significantly impact the final structural integrity of the part. This implies that using experimental data attained through destructive testing is not always viable. In this study, predictive models based on the rapid prediction of the required extrusion force and mechanical properties of printed parts are proposed, selecting a subset of the most representative printing parameters during the printing process as the domain of interest. Data obtained from the in-line sensor-equipped 3D printers were used to train several different predictive models. By comparing the coefficient of determination (R<sup>2</sup>) of the response surface method (RSM) and five different machine learning models, it is found that the support vector regressor (SVR) has the best performance in this data volume case. Ultimately, the ML resources developed in this work can potentially support the application of AM technology in the assessment of part structural integrity through simulation and can also be integrated into a control loop that can pause or even correct a failing print if the expected filament force-speed pairing is trailing outside a tolerance zone stemming from ML predictions.https://www.mdpi.com/2504-4494/7/5/186additive manufacturingmaterial extrusionmachine learningin-line sensorproperties prediction |
| spellingShingle | Zijie Liu Gerardo A. Mazzei Capote Evan Grubis Apoorv Pandey Juan C. Blanco Campos Graydon R. Hegge Tim A. Osswald Predicting Properties of Fused Filament Fabrication Parts through Sensors and Machine Learning Journal of Manufacturing and Materials Processing additive manufacturing material extrusion machine learning in-line sensor properties prediction |
| title | Predicting Properties of Fused Filament Fabrication Parts through Sensors and Machine Learning |
| title_full | Predicting Properties of Fused Filament Fabrication Parts through Sensors and Machine Learning |
| title_fullStr | Predicting Properties of Fused Filament Fabrication Parts through Sensors and Machine Learning |
| title_full_unstemmed | Predicting Properties of Fused Filament Fabrication Parts through Sensors and Machine Learning |
| title_short | Predicting Properties of Fused Filament Fabrication Parts through Sensors and Machine Learning |
| title_sort | predicting properties of fused filament fabrication parts through sensors and machine learning |
| topic | additive manufacturing material extrusion machine learning in-line sensor properties prediction |
| url | https://www.mdpi.com/2504-4494/7/5/186 |
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