# AI vision quality control on high-speed electronics lines

Manual inspection couldn't keep up with high-speed lines for precision smartphone components, so defects escaped and good parts were rejected. Multi-camera computer vision that classifies defects at line speed, flags novel defects for review and quarantines suspect supplier lots.

- **Sector**: Consumer electronics
- **Region**: Global
- **Disciplines**: Computer vision · Edge AI · MLOps
- **Practices**: [AI & machine learning](https://www.winapps.co.in/services/ai-ml/)
- **Client**: Under NDA. References available on a call.

## The challenge

A manufacturer of precision smartphone components needed inspection that could run at full line speed. Human inspectors missed defects at that pace, while traditional machine vision struggled with new defect types, lighting changes and material variation, rejecting good parts and stopping lines.

## Our approach

We deployed vision-transformer models on edge hardware beside each line, classifying defects such as micro-cracks, contamination and assembly errors frame by frame. An anomaly detector flags defects the model hasn't seen before for human review, and verified rejects feed retraining without stopping production.

Inspection results go to the MES, which diverts defective parts and quarantines suspect supplier lots, and supplier scorecards push root-cause fixes upstream.

## Architecture

- **Line**: Multi-camera image capture, Smart reject conveyor
- **Edge**: Defect classification, Novel-defect (anomaly) detection
- **Learning loop**: Human review of flagged parts, Retraining across sites, Model versioning
- **Integration**: MES rejects, Supplier quality scorecards, Lot quarantine

Illustrative architecture, simplified to protect client confidentiality.

## Results

Outcome details for this engagement are shared under NDA. We're happy to walk through results and put you in touch with a reference on a call.

## Stack

- PyTorch
- Vision transformers
- Edge inference
- MLOps

Next case study: [Generative AI design for heavy construction equipment](https://www.winapps.co.in/work/generative-design-heavy-equipment/) ([Markdown](https://www.winapps.co.in/work/generative-design-heavy-equipment.md))

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