From raw manufacturing data to actionable optimization insights — here is the end-to-end flow.
Factory sensor data and casting parameters are collected from the production floor. This includes thermal readings, flow rates, material properties, and operational metrics across the manufacturing pipeline.
Trained machine learning models process the input data through two specialized pipelines — a casting quality optimizer that uses Bayesian sampling to find optimal parameters, and a factory health classifier that predicts operational status.
A FastAPI backend serves predictions through RESTful endpoints. The frontend sends parameter configurations, receives optimized results, defect probabilities, and factory health predictions in real-time.
Results are displayed as interactive cards with quality scores, optimal parameter values, risk badges, and performance indicators. The AI copilot provides contextual explanations of the results and suggests next actions.
Bayesian parameter search across temperature, speed, and material combinations to maximize quality scores.
Predict operational status from 16 metrics spanning production, quality, maintenance, and energy efficiency.
Ask questions about model outputs, casting trade-offs, and defect mitigation strategies in natural language.