Computer Vision for Zero-Defect Assembly Lines
Enabling zero-defect manufacturing through edge based visual quality control
The Challenge
Our Approach
We deployed an Edge-based Computer Vision System trained on real production footage. Features included:
- Real-time video feed analysis to flag defective units on the assembly line
- Detection of micro-level inconsistencies using transfer learning from pre-trained models
- Integration with conveyor belt PLCs to auto-reject faulty products
- A no-code dashboard for QA leads to retrain models using new defect images
Latency and lighting variability were solved via hardware-optimized models running on NVIDIA Jetson devices.
Stats
Defect escape rate cut from 8.2% to 1.3%
The Outcome
- Reduced visual defect escape rate from 8.2% to under 1.3%
- Cut manual inspection time by 70%
- Increased first-pass yield by 19% in 60 days
- Built an internal “defect library” for future predictive maintenance use
Case Study
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