Home▸Process Analytical Technology (PAT)▸3D Machine Vision Tablet Defect Detection

📡 3D Machine Vision Tablet Defect Detection

The camera, feature-extraction wireframe, AI classifier and reject air-jet of an in-line tablet inspection line, rendered as orbitable 3D geometry above a running conveyor — the same speed-vs-accuracy math as the 2D original, now with a working sensitivity-threshold trade-off.

Process Analytical Technology (PAT)3DAdvanced60 FPS📱 Mobile-adapted

⚙ Under the hood

Tablets stream along a textured conveyor belt as flattened-sphere meshes in Three.js/WebGL, passing four zone markers matching the 2D original's stage layout: capture, feature extraction, classification and rejection. The classification step reuses the 2D model's speed-penalty formula verbatim — baseAccuracy = 0.995 − speedPenalty × 0.13, where speedPenalty scales with conveyor speed — and extends it with a genuine sensitivity-threshold slider that trades recall against precision: a lower threshold catches more true defects at the cost of more false alarms, a higher threshold reduces nuisance rejects but risks missing marginal defects, exactly the trade-off described for real production classifiers. Feature extraction renders as a wireframe overlay sphere, defect types (chip, crack, discoloration, capping, coating gap, particulate) attach as distinct small meshes revealed at classification, and a physical air-jet nozzle diverts flagged tablets into a reject bin while passed tablets continue into a good bin — orbit the scene to watch the line from any angle.

Machine VisionPATQuality Control

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install