Project Details
Description
This project combines furnace characterisation in the FutureForge facility with the development of advanced, AI-enhanced induction hardening models. Furnace trials using instrumented parts will generate live thermal data to assess efficiency, carbon cost, and temperature uniformity, enabling analytical furnace models and informing future investment decisions.
Key findings
Developed a hybrid AI–FEM modelling framework to optimise induction heating and hardening processes, enabling accurate prediction of thermal and hardened layer profiles with significantly reduced computation time. The approach integrated neural networks trained on FEM simulation data, providing rapid process optimisation capabilities for industrial applications.
| Short title | AFRC-CORE-06951 |
|---|---|
| Status | Finished |
| Effective start/end date | 16/05/24 → 16/10/25 |
Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
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Transvalor/Bifrangi/AFRC - case study - Simulation of the induction hardening process applied to crankshaft manufacturing
Andreu, A., Huang, J., Coulbeck, T. & Perchat, E., 1 Dec 2025.Research output: Working paper/Preprint/Pre-registration › Case study
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AFRC-PRS02842-CORE06951-D5.1-Literature review on the neural network approach
Andreu, A. & Huang, J., 17 Jan 2025, Glasgow.Research output: Book/Report › Commissioned report