Hybrid physical model and status data-driven approach for quality-reliable digital light processing 3D printing
Existing methods for detecting anomalies in digital light processing (DLP) 3D printing and performing in-situ repairs can reduce most defects and improve success rates. However, since printing control parameters cannot adapt to real-time printing conditions, anomalies may persist across successive l...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Virtual and Physical Prototyping |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17452759.2025.2460784 |
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| _version_ | 1825208860760080384 |
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| author | Lidong Zhao Xueyun Zhang Zhi Zhao Limin Ma Lifang Wu |
| author_facet | Lidong Zhao Xueyun Zhang Zhi Zhao Limin Ma Lifang Wu |
| author_sort | Lidong Zhao |
| collection | DOAJ |
| description | Existing methods for detecting anomalies in digital light processing (DLP) 3D printing and performing in-situ repairs can reduce most defects and improve success rates. However, since printing control parameters cannot adapt to real-time printing conditions, anomalies may persist across successive layers, and continuous repairs could ultimately lead to printing failure. Therefore, achieving stable printing quality requires integrating anomaly detection with the dynamic adjustment of control parameters. In this paper, we propose a hybrid approach that combines physical models with real-time status data to achieve quality-reliable DLP 3D printing. We developed a status data acquisition scheme to monitor printing status, including the downward force exerted on the printing platform, curing temperatures, resin levels, and surface morphology. Analyzing the collected data provides both status and anomaly information, enabling in-situ repair strategies to address abnormalities with minimal disruption to the printing process. Additionally, an Extended Kalman Filter integrates status data with physical models to dynamically optimise printing parameters. Experimental results show that the proposed scheme effectively addresses typical anomalies, optimises printing times, and significantly improves success rates while preserving the mechanical performance of printed models. Furthermore, the approach adapts to varying printing status, resin materials, and models. |
| format | Article |
| id | doaj-art-6ad9ce387c514193b832cf34fc41a09b |
| institution | Kabale University |
| issn | 1745-2759 1745-2767 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Virtual and Physical Prototyping |
| spelling | doaj-art-6ad9ce387c514193b832cf34fc41a09b2025-02-06T19:57:10ZengTaylor & Francis GroupVirtual and Physical Prototyping1745-27591745-27672025-12-0120110.1080/17452759.2025.2460784Hybrid physical model and status data-driven approach for quality-reliable digital light processing 3D printingLidong Zhao0Xueyun Zhang1Zhi Zhao2Limin Ma3Lifang Wu4Faculty of Information Technology, Beijing University of Technology, Beijing, People's Republic of ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, People's Republic of ChinaFaculty of Materials and Manufacturing, Beijing University of Technology, Beijing, People's Republic of ChinaFaculty of Materials and Manufacturing, Beijing University of Technology, Beijing, People's Republic of ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, People's Republic of ChinaExisting methods for detecting anomalies in digital light processing (DLP) 3D printing and performing in-situ repairs can reduce most defects and improve success rates. However, since printing control parameters cannot adapt to real-time printing conditions, anomalies may persist across successive layers, and continuous repairs could ultimately lead to printing failure. Therefore, achieving stable printing quality requires integrating anomaly detection with the dynamic adjustment of control parameters. In this paper, we propose a hybrid approach that combines physical models with real-time status data to achieve quality-reliable DLP 3D printing. We developed a status data acquisition scheme to monitor printing status, including the downward force exerted on the printing platform, curing temperatures, resin levels, and surface morphology. Analyzing the collected data provides both status and anomaly information, enabling in-situ repair strategies to address abnormalities with minimal disruption to the printing process. Additionally, an Extended Kalman Filter integrates status data with physical models to dynamically optimise printing parameters. Experimental results show that the proposed scheme effectively addresses typical anomalies, optimises printing times, and significantly improves success rates while preserving the mechanical performance of printed models. Furthermore, the approach adapts to varying printing status, resin materials, and models.https://www.tandfonline.com/doi/10.1080/17452759.2025.2460784Digital light processingphysical modelstatus monitoringcontrol parametersdynamic control |
| spellingShingle | Lidong Zhao Xueyun Zhang Zhi Zhao Limin Ma Lifang Wu Hybrid physical model and status data-driven approach for quality-reliable digital light processing 3D printing Virtual and Physical Prototyping Digital light processing physical model status monitoring control parameters dynamic control |
| title | Hybrid physical model and status data-driven approach for quality-reliable digital light processing 3D printing |
| title_full | Hybrid physical model and status data-driven approach for quality-reliable digital light processing 3D printing |
| title_fullStr | Hybrid physical model and status data-driven approach for quality-reliable digital light processing 3D printing |
| title_full_unstemmed | Hybrid physical model and status data-driven approach for quality-reliable digital light processing 3D printing |
| title_short | Hybrid physical model and status data-driven approach for quality-reliable digital light processing 3D printing |
| title_sort | hybrid physical model and status data driven approach for quality reliable digital light processing 3d printing |
| topic | Digital light processing physical model status monitoring control parameters dynamic control |
| url | https://www.tandfonline.com/doi/10.1080/17452759.2025.2460784 |
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