How a steel component manufacturer achieved 99.2% defect detection accuracy and reduced scrap waste by 38% using computer vision and deep learning.
SteelWorks Quality Co. manufactures high-specification steel components for automotive and construction sectors. Quality control was historically manual and inconsistent — visual inspectors would examine thousands of parts daily, leading to missed defects (2-3% false negatives) and high scrap rates due to costly rework and customer returns.
GridMatrix deployed a computer vision system powered by deep learning to automate 100% of incoming and in-process inspections, achieving 99.2% accuracy while reducing labor costs and scrap waste significantly.
The system uses industrial-grade cameras with 5MP resolution and standardized lighting to capture part images at 2 FPS (frames per second). Deep learning models deployed on NVIDIA Jetson edge devices perform real-time inference with <100ms latency. Defect types detected include surface scratches (0.5mm+), dents, cracks, and corrosion. Results are logged to a cloud database and visualized via a real-time quality dashboard. Integration with the PLC enables automated reject arms to physically separate defective parts from the production line, eliminating manual intervention.
SteelWorks Quality Co. successfully automated 100% of quality inspections, achieving 99.2% defect detection accuracy. By eliminating manual inspection bottlenecks and reducing scrap waste by 38%, the company improved profitability while delivering consistently higher-quality products to customers. The system now processes 15,000+ parts daily with zero human intervention, freeing inspectors to focus on process improvement and root cause analysis.
