Sanezoo Catalog

 Visual quality control • 3d picking & feeding • Robotic inspection

Chip Detection

AI-Based Detection of Machining Debris on Critical Surfaces

Overview

This neural network uses artificial intelligence to detect chips, swarf, and other residual debris left behind from machining processes. It ensures surfaces are clean and free from contaminants that could impact performance, safety, or hygiene.

Why It Matters

Undetected chips can cause malfunctions, clog fluid or air channels, contaminate sterile environments, or damage components during assembly. Automated chip detection ensures every part meets quality standards before continuing down the line.

Where It’s Used

  • Healthcare / Medical
  • Chemical Production
  • Food and Beverage
  • Biolabs
  • Automotive
  • Aerospace

Capabilities

  • AI-powered detection of residual chips and machining swarf
  • Works on both open and partially occluded surfaces
  • Adjustable sensitivity for different part types and debris sizes
  • Multi-zone image analysis per frame
  • Compatible with boroscopic or top-down camera views
  • Enables automated rejection, cleaning triggers, or operator alerts

Chip Detection in Channels

AI-Based Detection of Residual Particles in Machined Grooves

The system compares captured images to detect the presence of chips inside channels. In Image 1, no particles are detected, the part is OK. In Image 2, the detected chip triggers an automatic NOK classification.

Example Use Case

In a food processing equipment factory, parts are inspected after CNC machining. The neural network detects a small metal shaving inside a fluid channel. The part is flagged for cleaning before final assembly, avoiding contamination and preventing costly rework.