Build the automated pass/fail logic that turns raw deviation data into actionable QC decisions. Your anomaly detection models sit at the decision boundary – they determine whether a weld passes, a beam is misaligned, or a machined part is out of spec. This drives the upsell from "AR inspection viewer" to "AI-powered QC automation."
System Ownership
- Primary: Anomaly detection models (deviation classification: pass / marginal / fail)
- Primary: Time-series quality trend analysis (degradation detection across inspection history)
- Primary: Automated QC decision engine (tolerance-aware pass/fail from 3D deviation data)
- Primary: Model training and evaluation pipeline (data prep, training, validation, A/B testing)
- Secondary interface: CAD Geometry Engineer (they provide GD&T; tolerances and feature maps you evaluate against)
- Secondary interface: Backend Engineer (they store inspection data you train on and serve predictions through)
- Does NOT own: Point cloud registration (CV team), edge inference runtime (Edge AI team), dashboard visualisation (Full Stack team),
data storage (Backend team)
What You Will Build
- Deviation classification model – Given a 3D deviation map (per-point distances between aligned scan and CAD reference), classify each region as pass, marginal, or fail. Account for GD&T; tolerances: a 2mm deviation on a rough surface is pass; a 0.5mm deviation on a precision bore is fail.
- Anomaly detection for inspection time-series – Track deviation patterns across serial inspections. Detect: systematic drift (a machine tool wearing down), sudden shifts (fixture misalignment), seasonal patterns (thermal expansion in summer).
- Root cause suggestion engine – When an anomaly is detected, correlate with contextual data (operator, shift, machine, material batch, ambient temperature) to suggest probable root causes.
- Model training pipeline – Automated pipeline: data collection from inspection history → feature engineering → model training → offli