There are copious amounts of data and information regarding wear on gears but less analysis with wear on gear tools. This article in Gear Solutions magazine by (Melina Kamratowski, Christopher Jensen, Mareike Davidovic and Thomas Bergs) aims to resolve this demonstrating how AI is transforming analysis.
Modern gear manufacturing uses cutting tools that slowly wear down, which can hurt quality and raise costs. Detecting this wear early is important, but it’s usually done manually—making it slow and sometimes inconsistent.
This study looks at how computer vision (AI that analyses images) can automatically detect and measure tool wear. The researchers compared two approaches:
- Traditional image processing (basic techniques like detecting edges), and
- Deep learning (more advanced AI trained to recognise patterns).
They found traditional methods weren’t very reliable, especially when lighting or surface conditions changed. Deep learning, however, was much better at spotting wear accurately.
The system they developed works by:
- Taking microscope images of the tool
- Using AI to highlight worn areas
- Measuring how severe the wear is
Tests showed the automated system produced results very close to human measurements, but much faster- analysing images in seconds.
AI-powered image analysis can make monitoring tool wear quicker, more consistent, and potentially cheaper than manual inspection. Find the complete article here.