Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/336722 
Year of Publication: 
2025
Citation: 
[Journal:] Journal of Intelligent Manufacturing [ISSN:] 1572-8145 [Volume:] 37 [Issue:] 2 [Publisher:] Springer US [Place:] New York, NY [Year:] 2025 [Pages:] 933-954
Publisher: 
Springer US, New York, NY
Abstract: 
Dislocation density-based models offer a physically grounded approach to modeling strain hardening in metal forming. Since these models are typically defined by Ordinary Differential Equations (ODEs), their accuracy is constrained by both, the model formulation and the parameter identification process. Machine Learning (ML) provides an alternative by allowing models to be constructed directly from experimental data, bypassing the accuracy limitations of explicitly defined models. However, applying ML to ODEs introduces the need for novel training techniques. This work presents a new approach for developing neural ODE models for flow curve description, utilizing a contact transformation to simplify the problem of learning an ODE into a learning a multivariate function.
Subjects: 
Machine learning
Flow curve
Forming
Finite element model
Dislocation density
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.