Aluminium Melt Chemistry Predictor
Predicts resulting melt chemistry from charge composition, alloying additions, and furnace conditions. Public-data reference implementation.
This model is a reference implementation. Industrial use requires validation on plant-specific data. Customer-specific fine-tuning remains private unless explicitly approved for publication.
Input schema
| Field | Type | Description |
|---|---|---|
| charge_composition | float[] · wt% | Primary + scrap composition |
| alloy_additions | float[] · kg | Master-alloy additions |
| melt_temp | float · °C | Melt temperature |
Output schema
| Field | Type | Description |
|---|---|---|
| melt_chemistry | float[] · wt% | Predicted element concentrations |
Training data & evaluation
Training data type
Public + synthetic melt records
Evaluation metrics
Limitations
- Calibrated to common wrought alloys; extend for casting alloys.
Discussion
- s.Validation note
s.iyer
ML researcher · 3d
Reproduced the reported metric on the public split with a fixed seed. Numbers line up within noise, so it's a good baseline to build on.
18 - l.Improvement
l.zhang
Process engineer · 1w
Adding an uncertainty head would make this far more useful for prioritization. Happy to open a PR against the reference repo.
11
Validate on your data, keep the result private
Reference models are a starting point. We can fine-tune privately on plant-specific data, with customer-owned IP and no public disclosure.