In this paper, we explore the application of Physics-Informed Neural Networks (PINNs) in parameter identification for continuum models of manufacturing systems. Although these models are invaluable for production planning at the factory level, the reliability of model-based decision-making strategies hinges significantly on accurate parameter estimation. We emphasize the distinct differences between PINNs and conventional parameter identification methods, particularly in terms of parameter sensitivities and uncertainty quantification. Our findings reveal that the PINN-based identification framework results in more significant parameter uncertainties. Consequently, this prompts us to discuss the implications for experimental designs, system identification, and the pivotal role of smart data.
Parameter Identification in Manufacturing Systems Using Physics-Informed Neural Networks
Lect.Notes Mechanical Engineering
European Symposium on Artificial Intelligence in Manufacturing ; 2023 ; Kaiserslautern, Germany September 19, 2023 - September 19, 2023
27.06.2024
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
manufacturing systems , physics-informed neural network , partial differential equations , distributed parameter system , parameter sensitivities , uncertainty quantification Engineering , Robotics and Automation , Industrial and Production Engineering , Signal, Image and Speech Processing , Industrial Chemistry/Chemical Engineering
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