AI & Machine Learning in Inspection

What this topic covers

Pattern recognition and anomaly detection as decision-support tools.

Core testing ideas

AI can classify images or signals and prioritize unusual results.

Training data quality and representation of real defects strongly affect performance.

Human review and validation remain important for high-consequence decisions.

Measurement and method limits

Machine vision and AI can scale inspection but can also scale systematic false accepts or false rejects.

Evidence and traceability

Digital records need stable part identity, units, revisions and audit trails.

Using the result

Robots and automated motion systems require engineered safeguarding outside this site's procedural scope.