AI Identification and Feedback Module for Appearance Quality of Metal Products 2026/09/29

Introductions

Traditional visual inspection are easily affected by metal surface reflections and uneven light source. It leads to misjudgment issues, especially inspects on irregularly shaped 3D metal parts. Factors like low detection rate, unstable effects, and limited applications cause difficulty to achieve the goals of intelligent inspection. Using AI for detection helps overcome problems related to metal surface reflections and uneven lighting, and interference from the shadowing.

Technical Advantages and Features

• AI Image Recognition Technology: AI deep learning module trains on complex defect features, such as random wood grains and helical gear teeth. The AI module adapts to various environments and production lines. The scope is to broaden to varies industries, such as food and textile inspection.
• Dynamic Optimal Lighting Layout Imaging: Combining image filtering transformations with blur analysis enables light and shadow detection, overcoming issues like reflections and uneven illumination in dynamic imaging. Based on light and shadow detection, dynamic adjustment reduces reflections in multi-angle imaging by light source placement and detection angles.
• 3D Helical Gear Inspection: A specialized detection model for 3D helical gears achieves a defect detection rate of 98% (industry average 80%). Breaking through technological bottlenecks by automatically detects challenging defects such as tooth surface black spots, tip collisions, and broken teeth, identifying flaws as small as ≥ 0.1 mm.

Industrial Applications and Business Opportunities

• Industry Applications
  • Metal Product Processing Industry (e.g., Plumbing Supplies, Gear Manufacturing, Powder Metallurgy).
  • Intelligent Factory.
• Application Examples
  • The technology, integrated into specialized gear inspection equipment, has been successfully implemented by electric vehicle gear production line. It reduces inspection time by 50%, tripling daily production capacity, and cutting annual quality control costs by about 50%.