Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/336722 
Erscheinungsjahr: 
2025
Quellenangabe: 
[Journal:] Journal of Intelligent Manufacturing [ISSN:] 1572-8145 [Volume:] 37 [Issue:] 2 [Publisher:] Springer US [Place:] New York, NY [Year:] 2025 [Pages:] 933-954
Verlag: 
Springer US, New York, NY
Zusammenfassung: 
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.
Schlagwörter: 
Machine learning
Flow curve
Forming
Finite element model
Dislocation density
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