Automated Steel Defect Classification
The client's quality classification process relied heavily on manual inspection and subjective operator interpretation of defects. This lack of standardization created significant inconsistencies in the final product grading. Furthermore, the rigidity of manual classification prevented the commercial department from systematically matching available inventory with specific customer tolerance profiles, leading to the devaluation of viable stock and operational inefficiencies.
We developed a deterministic rule-based engine that fully automates the quality decision workflow. The system digitizes the plant's complex metallurgical and dimensional decision matrix to eliminate subjectivity. By processing granular data the engine objectively assigns quality grades. The architecture includes a versioned rule repository (SQL) to ensure full traceability of every decision, facilitating auditability and continuous system maintenance.
The solution eradicates human variability in quality control, transforming a subjective task into a data-driven standard. Strategically, it maximizes revenue by enabling dynamic inventory allocation: the system can identify material that, while not meeting standard generic grades, fits perfectly within the specific technical constraints of distinct client orders. This reduces material declassification/scrap rates and optimizes the commercial yield of the production line.
Railway Ballast Contamination Detection
Industrial Energy Forecasting
