Bayesian optimization with Gaussian-process-based active machine learning for improvement of geometric accuracy in projection multi-photon 3D printing
Abstract Multi-photon polymerization is a well-established, yet actively developing, additive manufacturing technique for 3D printing on the micro/nanoscale. Like all additive manufacturing techniques, determining the process parameters necessary to achieve dimensional accuracy for a structure 3D pr...
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| Format: | Article |
| Language: | English |
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Nature Publishing Group
2025-01-01
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| Series: | Light: Science & Applications |
| Online Access: | https://doi.org/10.1038/s41377-024-01707-8 |
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| author | Jason E. Johnson Ishat Raihan Jamil Liang Pan Guang Lin Xianfan Xu |
| author_facet | Jason E. Johnson Ishat Raihan Jamil Liang Pan Guang Lin Xianfan Xu |
| author_sort | Jason E. Johnson |
| collection | DOAJ |
| description | Abstract Multi-photon polymerization is a well-established, yet actively developing, additive manufacturing technique for 3D printing on the micro/nanoscale. Like all additive manufacturing techniques, determining the process parameters necessary to achieve dimensional accuracy for a structure 3D printed using this method is not always straightforward and can require time-consuming experimentation. In this work, an active machine learning based framework is presented for determining optimal process parameters for the recently developed, high-speed, layer-by-layer continuous projection 3D printing process. The proposed active learning framework uses Bayesian optimization to inform optimal experimentation in order to adaptively collect the most informative data for effective training of a Gaussian-process-regression-based machine learning model. This model then serves as a surrogate for the manufacturing process: predicting optimal process parameters for achieving a target geometry, e.g., the 2D geometry of each printed layer. Three representative 2D shapes at three different scales are used as test cases. In each case, the active learning framework improves the geometric accuracy, with drastic reductions of the errors to within the measurement accuracy in just four iterations of the Bayesian optimization using only a few hundred of total training data. The case studies indicate that the active learning framework developed in this work can be broadly applied to other additive manufacturing processes to increase accuracy with significantly reduced experimental data collection effort for optimization. |
| format | Article |
| id | doaj-art-bb876693a13e4c85b4325aaf83d7053d |
| institution | Kabale University |
| issn | 2047-7538 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Light: Science & Applications |
| spelling | doaj-art-bb876693a13e4c85b4325aaf83d7053d2025-01-26T12:53:15ZengNature Publishing GroupLight: Science & Applications2047-75382025-01-0114111210.1038/s41377-024-01707-8Bayesian optimization with Gaussian-process-based active machine learning for improvement of geometric accuracy in projection multi-photon 3D printingJason E. Johnson0Ishat Raihan Jamil1Liang Pan2Guang Lin3Xianfan Xu4School of Mechanical Engineering, Purdue UniversitySchool of Mechanical Engineering, Purdue UniversitySchool of Mechanical Engineering, Purdue UniversitySchool of Mechanical Engineering, Purdue UniversitySchool of Mechanical Engineering, Purdue UniversityAbstract Multi-photon polymerization is a well-established, yet actively developing, additive manufacturing technique for 3D printing on the micro/nanoscale. Like all additive manufacturing techniques, determining the process parameters necessary to achieve dimensional accuracy for a structure 3D printed using this method is not always straightforward and can require time-consuming experimentation. In this work, an active machine learning based framework is presented for determining optimal process parameters for the recently developed, high-speed, layer-by-layer continuous projection 3D printing process. The proposed active learning framework uses Bayesian optimization to inform optimal experimentation in order to adaptively collect the most informative data for effective training of a Gaussian-process-regression-based machine learning model. This model then serves as a surrogate for the manufacturing process: predicting optimal process parameters for achieving a target geometry, e.g., the 2D geometry of each printed layer. Three representative 2D shapes at three different scales are used as test cases. In each case, the active learning framework improves the geometric accuracy, with drastic reductions of the errors to within the measurement accuracy in just four iterations of the Bayesian optimization using only a few hundred of total training data. The case studies indicate that the active learning framework developed in this work can be broadly applied to other additive manufacturing processes to increase accuracy with significantly reduced experimental data collection effort for optimization.https://doi.org/10.1038/s41377-024-01707-8 |
| spellingShingle | Jason E. Johnson Ishat Raihan Jamil Liang Pan Guang Lin Xianfan Xu Bayesian optimization with Gaussian-process-based active machine learning for improvement of geometric accuracy in projection multi-photon 3D printing Light: Science & Applications |
| title | Bayesian optimization with Gaussian-process-based active machine learning for improvement of geometric accuracy in projection multi-photon 3D printing |
| title_full | Bayesian optimization with Gaussian-process-based active machine learning for improvement of geometric accuracy in projection multi-photon 3D printing |
| title_fullStr | Bayesian optimization with Gaussian-process-based active machine learning for improvement of geometric accuracy in projection multi-photon 3D printing |
| title_full_unstemmed | Bayesian optimization with Gaussian-process-based active machine learning for improvement of geometric accuracy in projection multi-photon 3D printing |
| title_short | Bayesian optimization with Gaussian-process-based active machine learning for improvement of geometric accuracy in projection multi-photon 3D printing |
| title_sort | bayesian optimization with gaussian process based active machine learning for improvement of geometric accuracy in projection multi photon 3d printing |
| url | https://doi.org/10.1038/s41377-024-01707-8 |
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