Overview
A major electronics manufacturer was struggling with quality control on their PCB assembly lines. Manual inspection was slow, inconsistent, and resulted in a 30% false rejection rate, causing significant rework costs and customer dissatisfaction.
The Challenge
The inspection process had multiple pain points:
- •High variability in PCB designs requiring flexible inspection criteria
- •Microscopic defects often invisible to tired human inspectors
- •30% false rejection rate causing unnecessary rework
- •Production bottleneck due to slow inspection speeds
- •Inconsistent standards across different shifts and inspectors
"We were rejecting good boards and missing defective ones. The inconsistency was hurting both our costs and customer relationships."
Our Solution
1. Imaging System Design
Installed optimized hardware:
- •High-resolution cameras (20MP+)
- •Multi-angle LED illumination
- •Automated board positioning
- •Consistent imaging conditions
2. Data Collection & Labeling
Built comprehensive training dataset:
- •50,000+ images covering all defect types
- •Expert-labeled with detailed annotations
- •Balanced representation of defect categories
- •Captured acceptable variations to reduce false positives
3. Deep Learning Models
Developed specialized detection models:
| Model Component | Purpose |
|---|---|
| Object Detection | Component localization |
| Classification CNN | Defect type identification |
| Attention Mechanisms | Focus on critical regions |
| Ensemble Voting | Robust final decisions |
4. Edge Deployment
Optimized for real-time inference:
- •NVIDIA Jetson edge devices
- •<100ms inference time per board
- •No production delays
- •Local processing for data security
5. Feedback Loop
Continuous improvement system:
- •Operator flagging of edge cases
- •Weekly model retraining
- •A/B testing of model versions
- •Performance monitoring dashboard
Technical Stack
Framework: PyTorch
Vision: OpenCV, albumentations
Edge: NVIDIA Jetson AGX
Container: Docker
MLOps: MLflow
API: FastAPI
Results
The system delivered exceptional improvements:
- •99.7% defect detection rate (up from 70%)
- •5% false rejection rate (down from 30%)
- •80% faster inspection throughput
- •₹1.2 Crore annual savings in rework costs
- •65% reduction in customer complaints
Defect Categories Detected
- •Solder bridges and shorts
- •Missing components
- •Misaligned components
- •Solder insufficiency
- •Lifted leads
- •Polarity errors
- •Damaged components
Key Takeaways
- •Quality training data is more important than model complexity
- •Edge deployment enables real-time inspection without latency
- •Feedback loops ensure continuous improvement
- •Reducing false positives is as important as catching defects
- •Clear ROI makes the business case compelling
