Track: Artificial Intelligence

Abstract

Worm gears are used in industries where fast stopping and breaking applications such as lifts, hoists, and elevators. Worm gears are also used in machine tools. Worm gear sets are typically used in the gearbox. Worm gear motors offer quietness when compared to other types of equipment. They require small gearboxes, and they are increasingly used in space-saving applications. Inspection of worm gears is significant before they are deployed into service. This would ensure that they will not fail when put into service. Many of the existing gear inspection techniques are post-process in nature, and hence they are not fit for 100% inspection of gears. The current research work attempts to design and develop a novel approach for the non-contact assessment of worm gears using a vision system. Validation of the results obtained by the current method is done by comparing them with the results of the standard technique. As far as the author’s knowledge goes, no research article reported on the worm gear inspection using a vision system. In this context, the present research assumes special significance. The accuracy obtained by the current process is 98%. The method has shown very high repeatability. The way finds application in the online inspection of worm gears. The research investigation revealed that the technique could detect damaged teeth and other defects. Wear, scoring, pitting, and fracture, with a classification accuracy from 93% to 100%.