Is Your Process a Good Fit for AI Visual Inspection? – A Self-Assessment for Engineers

1. Parts are in roughly consistent position and orientation at the inspection point.

2. The relevant surfaces are clearly visible to a camera without needing to move the part.

3. Parts are transported on a stable carrier (conveyor, fixture, tray) rather than loosely.

4. Parts are separated enough that they do not overlap in the camera's field of view.

5. There is mechanical access for mounting cameras and lighting at or near the inspection point.

6. The most critical defects are primarily visual (surface, geometry, assembly, labeling).

7. A trained human, with good lighting and enough time, can reliably spot the key defects.

8. Defects are present on external surfaces that a camera can see (no destructive inspection needed).

9. You have, or could collect, clear examples of both good and defective parts for key defect categories.

10. The defects matter enough (safety, function, customer complaints, brand) to justify further work.

11. Line speed (or cycle time) is known and relatively stable, or varies within a known range.

12. There is a clear time window between "part in position" and the point where a decision is needed.

13. Current inspection tasks are a bottleneck or at risk of becoming one as volume increases.

14. You have, or can define, a target decision latency (for example, within a few hundred milliseconds).

15. Camera triggers could be tied to encoders or PLC signals if needed.

16. Strong, uncontrolled lighting changes (direct sunlight, flashing beacons) at the inspection point are limited or can be mitigated.

17. Glare and reflections on the part surface can be reduced with lighting or camera angle changes if needed.

18. Dust, mist, or steam are unlikely to frequently obscure the camera's field of view or can be managed with enclosures.

19. Camera mounting locations can avoid the worst vibration or be mechanically isolated.

20. There is space and power for dedicated inspection lighting if required.

21. Scrap from this process is material in cost or volume.

22. Customer complaints, returns, or chargebacks have been linked to missed visual defects.

23. 100% manual inspection is either not feasible or too costly to scale further.

24. A reduction in escapes, even by a modest amount, would meaningfully reduce downstream cost or risk.

25. You have a rough idea of what "good" would look like (for example, scrap or escape targets).

26. There is an existing reject or diversion mechanism that can be triggered electronically.

27. A PLC, SCADA system, or line controller already collects key signals (part present, encoder, line speed).

28. You would like to log inspection results and defect images for traceability or improvement work.

29. There is an MES, QMS, or data platform where inspection data could reasonably live.

30. Someone on the team (internal or partner) understands the current automation stack well enough to connect new systems.

31. Representative samples of both good and defective parts are available or can be collected without major disruption.

32. Defect definitions for the pilot scope can be aligned and written down in a simple guideline.

33. Core process conditions are reasonably stable over time (or planned changes are known).

34. There is a realistic way for operators or quality engineers to flag false rejects or misses for review.

35. Management is open to the idea of periodic model updates as products and processes evolve.

36. There is a clear owner for quality at this process (engineer, supervisor, quality manager).

37. Operators or inspectors would likely welcome support on repetitive inspection tasks if the system is transparent and reliable.

38. There is a realistic plan for who would monitor model performance and review flagged images or cases.

39. Existing work instructions or SOPs could be updated to include AI inspection steps.

40. There is openness to shifting people from 100% manual checking toward exception handling and improvement work.