Visual defect inspection — Ceramics
Deep-learning vision that flags surface and assembly defects at line speed. Delivered for a ceramics client in Anand.
- Location
- Anand
- Year
- 2021
- Duration
- 15 weeks
- Team
- 4 engineers
- 96%
- defect recall
- 40
- ms per part
- 61%
- fewer escapes
The challenge
Data existed but lived in spreadsheets and legacy systems, so decisions were made on instinct rather than evidence.
What we built
We ran a two-week discovery sprint, then built and fielded the system end to end on PyTorch, TensorRT and OpenCV, with operator training and an SLA.
Delivery timeline
Phase 1
Discovery sprint and site survey
Phase 2
Prototype built and tested in the Motera lab
Phase 3
Pilot on site with before/after measurement
Phase 4
Scale-out, training and handover
Highlights
- Remote monitoring and over-the-air updates included
- Runs on-premises; no data leaves the site unless agreed
- Designed, built and supported from Motera, Ahmedabad
- Before-and-after measurements agreed with the client up front
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