Automatic Gear Tooth Alignment in Vision Based Preventive Maintenance

Abstract

Thorough maintenance of industrial equipment is crucial for the finances of companies. Whereas the purchase of new tools can be an expensive business, reconditioning special gear often costs just a fraction. In this paper, preliminary steps for an accurate visual based preventive maintenance of hobbing wheels are investigated. To perform robust and reliable decisions about the wheel's condition, tool department specialists require precise taken captures of it. For this reason, a visual control cell is built, which depends on correctly aligned hobbing wheels in its image acquisition construction. The tool needs to be placed on a turn-table and rotated, so that a single tooth is centered in the field-of-view of the camera mounted on a robot arm. For this alignment task, three different main approaches with various preprocessing steps are investigated, a brute-force algorithm, an orb-feature approach and an image regression model. The results show that even a brute-force algorithm can be outperformed by a moderate deep neural network.