Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/323701 
Year of Publication: 
2024
Citation: 
[Journal:] Journal of Intelligent Manufacturing [ISSN:] 1572-8145 [Volume:] 36 [Issue:] 6 [Publisher:] Springer US [Place:] New York, NY [Year:] 2024 [Pages:] 4351-4363
Publisher: 
Springer US, New York, NY
Abstract: 
This work aims at a fast computational process model of the free-form bending process. It proposes a novel physically-informed machine learning model, which is trained with experimental data of bending constant radii and utilizes additional physical bending knowledge by integrating Timoshenko’s beam theory. The model is able to predict the resulting plastic deformation of the tube after exiting the die by computing an elastic representation of the tube’s deformation with beam theory at each time step. This elastic representation serves as input for a regression model similar to a partially connected neural network. This physically-informed machine learning model generalizes the constant training radii to complex bend geometries consisting of transitional sections and true spline geometries. It is compared to a benchmark finite element simulation and has an improved prediction quality for complex kinematics while reducing the computation time by four orders of magnitude.
Subjects: 
Freeform bending
Physically-informed neural networks
process model
Surrogate model
Geometry prediction
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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