Physics-informed and data-driven thermal modeling and temperature field prediction for thermal field-assisted direct ink writing with PDMS
Thermal field assistance enables in situ heating, improving the curing kinetics and printability of thermosetting polymers. However, it alters the temperature distribution during forming, affecting printing quality. Accurate temperature field prediction is essential for process optimization. Althoug...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Materials & Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S026412752500704X |
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| author | Xinhua Yao Yuhan Jiang Lixin Tian Xiang Li Yong He |
| author_facet | Xinhua Yao Yuhan Jiang Lixin Tian Xiang Li Yong He |
| author_sort | Xinhua Yao |
| collection | DOAJ |
| description | Thermal field assistance enables in situ heating, improving the curing kinetics and printability of thermosetting polymers. However, it alters the temperature distribution during forming, affecting printing quality. Accurate temperature field prediction is essential for process optimization. Although numerical simulations can capture temperature evolution in additive manufacturing (AM), they are time-consuming and storage-intensive, limiting their efficiency for real-time applications. To address this limitation, developing a rapid temperature prediction surrogate model based on deep learning becomes essential. In this study, a thermal simulation framework was developed specifically for thermal field-assisted AM (TFAM). Based on this framework, a surrogate model integrating physics-based and data-driven approaches was constructed and trained using multi-condition simulation datasets. To validate the simulation accuracy and evaluate the surrogate model, an experimental platform for thermosetting polymers was also established. The results showed that the average relative error between simulation and experiments was below 3 %. The proposed surrogate model outperformed other data-driven methods, achieving an average R2 above 0.99, with maximum RMSE and MAE of 0.3314 °C and 0.3174 °C. Notably, it maintained RMSE below 1 °C with only 10 % of the training data. Additionally, it reduced prediction time to seconds and storage to megabytes, significantly exceeding the efficiency of traditional simulations. |
| format | Article |
| id | doaj-art-4cfb014fb36a49eab5a452e8c6af757a |
| institution | Kabale University |
| issn | 0264-1275 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj-art-4cfb014fb36a49eab5a452e8c6af757a2025-08-20T03:24:29ZengElsevierMaterials & Design0264-12752025-08-0125611428410.1016/j.matdes.2025.114284Physics-informed and data-driven thermal modeling and temperature field prediction for thermal field-assisted direct ink writing with PDMSXinhua Yao0Yuhan Jiang1Lixin Tian2Xiang Li3Yong He4The State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China; Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China; Corresponding author at: The State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China.The State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China; Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, ChinaThe State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China; Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, ChinaThe State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China; Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, ChinaThe State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China; Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, ChinaThermal field assistance enables in situ heating, improving the curing kinetics and printability of thermosetting polymers. However, it alters the temperature distribution during forming, affecting printing quality. Accurate temperature field prediction is essential for process optimization. Although numerical simulations can capture temperature evolution in additive manufacturing (AM), they are time-consuming and storage-intensive, limiting their efficiency for real-time applications. To address this limitation, developing a rapid temperature prediction surrogate model based on deep learning becomes essential. In this study, a thermal simulation framework was developed specifically for thermal field-assisted AM (TFAM). Based on this framework, a surrogate model integrating physics-based and data-driven approaches was constructed and trained using multi-condition simulation datasets. To validate the simulation accuracy and evaluate the surrogate model, an experimental platform for thermosetting polymers was also established. The results showed that the average relative error between simulation and experiments was below 3 %. The proposed surrogate model outperformed other data-driven methods, achieving an average R2 above 0.99, with maximum RMSE and MAE of 0.3314 °C and 0.3174 °C. Notably, it maintained RMSE below 1 °C with only 10 % of the training data. Additionally, it reduced prediction time to seconds and storage to megabytes, significantly exceeding the efficiency of traditional simulations.http://www.sciencedirect.com/science/article/pii/S026412752500704XThermal field-assisted additive manufacturingThermal modelingTemperature field predictionData-drivenPhysical information |
| spellingShingle | Xinhua Yao Yuhan Jiang Lixin Tian Xiang Li Yong He Physics-informed and data-driven thermal modeling and temperature field prediction for thermal field-assisted direct ink writing with PDMS Materials & Design Thermal field-assisted additive manufacturing Thermal modeling Temperature field prediction Data-driven Physical information |
| title | Physics-informed and data-driven thermal modeling and temperature field prediction for thermal field-assisted direct ink writing with PDMS |
| title_full | Physics-informed and data-driven thermal modeling and temperature field prediction for thermal field-assisted direct ink writing with PDMS |
| title_fullStr | Physics-informed and data-driven thermal modeling and temperature field prediction for thermal field-assisted direct ink writing with PDMS |
| title_full_unstemmed | Physics-informed and data-driven thermal modeling and temperature field prediction for thermal field-assisted direct ink writing with PDMS |
| title_short | Physics-informed and data-driven thermal modeling and temperature field prediction for thermal field-assisted direct ink writing with PDMS |
| title_sort | physics informed and data driven thermal modeling and temperature field prediction for thermal field assisted direct ink writing with pdms |
| topic | Thermal field-assisted additive manufacturing Thermal modeling Temperature field prediction Data-driven Physical information |
| url | http://www.sciencedirect.com/science/article/pii/S026412752500704X |
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