Vision-based precision inspection system for profile and performance of small and micro gears using the intelligent image processing techniques and virtual master gears


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Abstract:As the product size become smaller, much smaller sized parts are increasingly required in many products fabrication such as cameras, camcorders, and electric appliances, etc. Small or micro gears whose sizes are about few hundred micrometers to few millimeters have been greatly required for the above products, as they become most efficient parts for power transmission. The small of micro gears are prone to geometrical and functioning inaccuracy during the micro fabrication processes, and thus the measurement system has been desired for the geometry and performance checking of gears. In this paper, a precision inspection system is developed for the gear checking, consisting of the optical vision microscope, vision image processing modules with subpixel accuracy, and the profile inspection software using the virtual master jigs/gears mathematically generated. For accurate analysis of the gear geometry from the captured gear image through the optical vision system, a highly accurate vision image processing algorithm has been implemented, where two dimensional subpixelling technique is developed having about one tenth subpixel accuracy. A mathematically generated mater gears/jigs are implemented: several core parameters of the gear profile are calculated from the comparison between the virtual master and the measured gears. Virtual precision ball jigs are simulated between gear teeth profile, and thus the runout parameter can be successfully evaluated from the virtual jig movement on the gear teeth image. The eccentricity parameters can also be calculated from the sinusoidal fitting of the virtual ball movement. The development precision inspection system for small or micro gears has been applied to practical gears of various range, and the essential gear parameters are successfully measured with sub micrometer accuracy.
Author: Heui Jae Pahk
Author Unit: Seoul National Univ. (South Korea)
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