OptiViz: AI-Powered Steel Surface Quality Control System
5/10/2025
Led the development of a computer vision system that automates steel surface defect detection across manufacturing lines. Combined deep learning, microservices, and real-time visualization to transform quality control.
Technical highlights
- Custom 3-layer CNN (DefectNet) in PyTorch identifying six critical defects: crazing, inclusion, patches, pitted surface, rolled-in scale, scratches
- Scalable microservices with Docker; FastAPI for high-performance inference endpoints
- Apache Airflow for automated data pipelines and retraining orchestration
- React dashboard with D3.js visualizations for real-time defect tracking and confidence scores
- Continuous data ingestion loop for ongoing model improvement
Impact
- Streamlined quality control via automated detection
- Reduced manual inspection time with live visualization
- Improved detection accuracy through continuous retraining
- Boosted efficiency with automated QA processes
Tech stack Python, PyTorch, FastAPI, React, D3.js, Docker, Apache Airflow, Computer Vision, Deep Learning, Microservices Architecture
Computer VisionPyTorchFastAPIReactD3.jsDockerAirflowMicroservices