| Autor: |
Mohammad Zhian Asadzadeh, Peter Raninger, Petri Prevedel, Werner Ecker, Manfred Mücke |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Applications in Engineering Science, Vol 5, Iss , Pp 100030- (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2666-4968 |
| DOI: |
10.1016/j.apples.2020.100030 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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