AI Defect Detection for CNC-Machined Parts

This defect detection system is built specifically for metal parts after CNC machining, using AI vision to help factories automate quality inspection. Manual visual inspection, under long working hours and high-throughput production lines, often leads to missed defects and misjudgments due to fatigue or inconsistent experience.

This system improves inspection consistency, shifting quality control from a labor-dependent process to a more precise and stable intelligent workflow.

Industrial cameras capture images of each part, and a deep-learning model analyzes them for scratches, dents, chipped edges, burrs, and other machining defects, marking the exact location of each defect to support downstream sorting, rejection, or labeling.

The architecture is modular, splitting image capture, preprocessing, model inference, and result output into independently adjustable modules, so the system can be flexibly configured for different client environments, equipment layouts, and product specifications.

To accommodate different part types and quality standards, the system offers parameterized configuration — operators can adjust acceptable defect size, confidence thresholds, and inspection rules directly in the admin backend, so AI judgments precisely match each factory's inspection standards. This lets the system adapt quickly when new products are introduced or specifications change, greatly improving deployment speed and operational flexibility.