Robot vision for automatic inspection of Permanent-magnets

Abstract

This project has been performed in cooperation with Rolls-Royce Marine. They have a new electric motor soon hitting the masses, and there is a hope that the magnets used in this motor can be automatically quality controlled with computer vision. This thesis takes on that challenge, and investigates possible technologies that can be used. Multi-view 3D reconstruction, laser stripe scanning and 2D image comparison techniques has been identified as promising candidates. Requirements for the magnets have been investigated, and the solutions that showed potential have been implemented. Optical character recognition has been identified as an important part of an automated quality control solution, as one need to register the serial number of each magnet. This has therefore also been implemented. The different implementations have then been tested and analyzed. It is concluded that while detecting chips from the corners and edges of the magnet could be possible with either laser scanning or 2D image comparison, the 2D image comparison is simpler and less computationally intensive, and therefore better suited for mass production. The tests where not able to safely detect the damages to the surface, but this might be possible with a better camera. Further work will be needed to conclude on whether this will be possible. Cracks where not detected with any of the solutions, and is likely the most difficult damage to detect. Due to the many difficulties encountered, a proposal has been made for a half-automated solution, where a human does visual inspection, the tolerance of the magnet is ensured automatically with a funnel system, and the serial number gets automatically scanned. For all implementations there are potentials for improvement. Trying different and better suited filtering techniques can improve the results of both the laser scanning, the 2D image techniques, and the optical character recognition. Using a better camera will also improve the results, as the camera that has been used has low resolution, high levels of noise, and lacks control of the auto focus, white balance, and aperture. If a 3D solution is chosen, one will also need a solution for how to identify a particular damage in the generated point cloud, as this has not been investigated in this report.