Electronics Manufacturer Cuts Inspection Time 80%

Project Overview

A leading electronics contract manufacturer was struggling to maintain quality while meeting customer demands for faster turnaround. Their manual inspection process—requiring trained operators to visually check every component and solder joint—was the production bottleneck.

With boards containing 500+ components and inspection taking over 4 seconds each, the manual process couldn't keep pace. Worse, inspector fatigue was causing defects to escape at an unacceptable rate of 150 ppm.

The AI Advantage

Traditional automated optical inspection (AOI) systems rely on explicit programming of acceptable and defective conditions. This approach struggles with the natural variation in electronics manufacturing and generates high rates of false calls that require human review.

AMD Automation implemented a deep learning-based approach:

Supervised Learning

The system was trained on thousands of images of both good and defective boards from actual production. The neural network learned to distinguish acceptable variation from actual defects without explicit programming.

Multi-Angle Imaging

Eight cameras positioned at different angles capture each board, enabling inspection of: - Components hidden under larger parts - Solder joints shadowed by tall components - Reflective surfaces that confuse single-camera systems

Continuous Learning

As new defect types emerge, additional training images can be added to improve detection. The system gets smarter over time.

Integration and Deployment

The system was designed for seamless integration into the existing SMT line:

Week 1-2: System installation during planned maintenance window Week 3-4: Training data collection from production boards Week 5-6: Model training and validation Week 7-8: Parallel operation with manual inspection for verification Week 9+: Full production deployment

By week 10, manual inspection was completely eliminated, with inspectors redeployed to other value-added roles.

Beyond Defect Detection

The real-time data collection enables proactive quality improvement:

Trend Detection

Statistical process control charts track defect rates by type, enabling early detection of process drift. Operators can adjust stencil alignment, reflow profiles, or paste volume before defects occur.

Traceability

Every inspection result is linked to the board's serial number, creating a complete quality record for warranty analysis and customer inquiries.

Supplier Quality

Incoming component quality issues are immediately visible through increased defect rates, enabling rapid supplier feedback.

Customer Testimonial

"We were skeptical that AI could match our best inspectors, but the data doesn't lie. Escape rate dropped from 150 ppm to under 5 ppm, and we're inspecting boards faster than we can make them. AMD Automation's team made the transition seamless."

— Quality Director, Electronics Contract Manufacturer