Hybrid modeling of induction hardening processes
A simple hybrid model, integrating observation (black-box) and physical knowledge (white-box), is employed to model an induction heating process. A neural network is used to estimate the unknown physical process parameter in the physical model. Most relevant to induction hardening is the temperature...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/237c6dcc4e8b454f96ce9fb5144601e9 |
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| Sumario: | A simple hybrid model, integrating observation (black-box) and physical knowledge (white-box), is employed to model an induction heating process. A neural network is used to estimate the unknown physical process parameter in the physical model. Most relevant to induction hardening is the temperature evolution in a layer under the surface of a sample, in our case a cylindrical sample. We show that with a hybrid model, in which a simple ordinary differential equation describes the heating rate, the experimental data can be approximated better than using a black-box only. The hybrid model extrapolates better and it is easier to interpret. The hybrid model can be used as a prediction tool to operate and optimize induction heating processes. |
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