Playbook
AI Defect Inspection Deployment
Deploy computer-vision defect inspection alongside existing human inspection processes.
Target outcome
Assisted inspection at target stations with measurable improvement in detection consistency and DHU trend visibility.
Steps
1.Baseline current inspection
Measure current DHU, inspection time and repeatability to set a fair baseline.
2.Define defect taxonomy
Align vision-model classes with your existing defect classification and severity rules.
3.Data collection and labelling
Collect representative images across shifts, shades and styles; label under a controlled protocol.
4.Model validation
Validate precision and recall on a hold-out set; run parallel human and AI inspection before cut-over.
5.Deploy and monitor
Integrate with MES/QMS; monitor drift and retrain on periodic cadence.
Risks to watch
- Biased training data missing rare defects
- Over-reliance on model output without human oversight
- Lighting and camera drift silently degrading accuracy
Decision guidance
Treat the steps above as a sequenced checklist, not a simultaneous programme. Each step should have a named owner, an evidence artefact and an explicit gate before the next step begins. Re-baseline the target outcome if scope or regulatory requirements change mid-programme.