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Physics-Informed Neural Networks (PINNs) for Improving a Thermal Model in Stereolithography Applications
- Source :
- Procedia CIRP. 104:1559-1564
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Stereolithography (SLA), additive manufacturing (3D printing) technique, is widely used nowadays for rapid prototyping and manufacturing (RP & M). This technique is driven by photo-polymerisation, which is an exothermal process. This may lead to thermal stresses significantly affecting the final quality of printed parts/products. To guarantee high-quality parts printed with the SLA technique, understanding the thermal behaviour is therefore crucial for optimizing the process. In this paper, the recent physics-informed neural network (PINN) methodology was employed to improve a physics-based model for predicting the thermal behaviour of SLA processes. The accuracy of the improved thermal model is demonstrated in this paper by comparing the predicted 2D temperature field with the 2D temperature field measured by a high-speed infrared thermal camera on parts printed on a production machine.
- Subjects :
- Rapid prototyping
Artificial neural network
business.industry
media_common.quotation_subject
Process (computing)
3D printing
Field (computer science)
law.invention
law
Thermal
General Earth and Planetary Sciences
Quality (business)
Process engineering
business
Stereolithography
General Environmental Science
media_common
Subjects
Details
- ISSN :
- 22128271
- Volume :
- 104
- Database :
- OpenAIRE
- Journal :
- Procedia CIRP
- Accession number :
- edsair.doi...........0082859fb483491c2b92f2fe8e8ff3b5