X-ray defect detection on carbon-fibre structural parts

The manufacturer produces carbon-fibre structural parts for performance brands. Every part is X-rayed and a trained inspector decides pass or fail. As volume grew across two factories and several X-ray machines, that manual step became the bottleneck and the source of the most expensive escapes.

Judgement varied by inspector and by shift, the hardest defect (delamination) was easy to miss, and images came from machines with different characteristics, so no single rule of thumb held across the line.

We started unsupervised. An anomaly model ranks every image by how unusual it looks, rolled up to the part by barcode, so inspectors review the riskiest images first instead of all of them. Each machine gets its own calibration, and same-machine comparisons are kept separate from cross-machine ones.

A review workstation lets inspectors confirm or overturn the ranking, and those labels feed a growing hard-case library. The system never auto-promotes a model or hides uncertainty: when it does not have the data, it says so.

• X-ray images from three machines across two factories
• Image-level anomaly ranking with barcode roll-up
• Human review workstation for inspector and adjudicator roles
• Per-machine calibration and a delamination hard-case library
• Labels captured during the trial; no auto-retrain, no auto-promote

One expensive problem, proven before it scales. The A1 to POC method, run in weeks not quarters.

Our delivery practices define human decision ownership, source and output traceability, data boundaries, change control and review evidence.

The credential is held personally by Vincent Lin.