Machine vision-based mapping method for angularly modified spur gears

For imported equipment with strictly confidential design data and for old transmission devices with lost design documents, gear mapping is an important technical means for replication and upgrade transformation. For the measurement and drafting of angularly modified spur gears, existing contact-based measurement methods have the disadvantages of cumbersome steps, unstable measurement accuracy, and low efficiency. To enhance the efficiency and precision of detection and measurement, this paper utilizes a machine vision approach to design a method for mapping angularly modified spur gears, which is efficient in detection and widely applicable. It enables the reverse engineering of design parameters such as gear modulus, pressure angle, displacement coefficient, and addendum reduction coefficient. First, this method combines image processing and point list segmentation methods to quickly identify the number of gear teeth and obtain geometric parameters of the gear; on this basis, a novel fitting tooth profile smooth search method is proposed, which abandons the traditional auxiliary positioning of the base circle, directly measures and calculates the gear common normal length; combined with reverse engineering technology, the gear parameter solving problem is transformed into an optimization problem, and the target function is constructed to achieve accurate solving of gear parameters. The actual gear mapping experiments were carried out and compared with the design parameters. Among them, the measurement results of modulus and pressure angle are highly consistent with the design values, and the maximum error of the displacement coefficient is only 0.0037. The average detection time for each gear is 8.6 seconds, proving that this method has advantages in improving detection efficiency and measurement accuracy, which can provide data support for gear replication and optimization. In addition, this method is compatible with the mapping of high displacement and standard spur gears, and has a wide range of applications. It has practical value for improving the application development of machine vision in the field of gear mapping.