Manufacturing Quality Control
Real-time defect detection using computer vision.
The Challenge
A high-volume automotive parts manufacturer was struggling with manual quality control processes that were slow, error-prone, and unable to keep up with production speeds. This resulted in a high rate of defects reaching customers, leading to costly recalls and damage to their brand reputation.
Our Solution
We implemented a real-time, AI-powered computer vision system directly on the assembly line. Using high-resolution cameras and a custom-trained convolutional neural network (CNN), the system inspects every part for microscopic cracks, surface imperfections, and dimensional inaccuracies. Defective parts are automatically flagged and diverted without slowing down production.
Key Outcomes
- Achieved 99.7% defect detection accuracy, surpassing human capabilities.
- Reduced the rate of defective parts reaching customers to nearly zero.
- Increased overall production throughput by 15% by eliminating manual inspection bottlenecks.
- Generated valuable data on defect types, enabling root cause analysis and process improvements.