Examination of teaching data for Hobbing-Machine-Diagnosis system (Comparison of classification performance of helical gears and spur gears and comparison of matrix images by hobbing simulation and actual hobbing) - Kunitoshi KAWANO; Daisuke IBA; Koichiro URIU; Ichiro MORIWAKI (Nihon Kikai Gakkai ronbunshu, 2022)
Various factors in hobbing processes affect profile and helix deviations of hobbed gears, which prevent problems on the hobbing machine from being analyzed using them. Identifying problems requires a system that allows itself to evaluate motion errors in a hobbing machine from them. The proposed one in this study has artificial intelligence for image analysis. Thus, the developed hobbing simulation provided the data on profile and helix deviations of hobbed gears derived from the motion errors in the hobbing process, which determined correlation coefficients to express the similarities of the simulated micro-geometries between tooth flanks of a hobbed gear. These correlation coefficients are color-coded according to their magnitude and converted into images to constitute training data for artificial intelligence. However, different motion errors could lead to similar ones in tests with helical gears. The present paper describes the comparisons between images obtained for spur and helical gears computed by hobbing simulation and the comparisons between ones produced from the simulated and actual micro-geometries. As a result, the motion error that causes similar images depends on gear geometries, i.e., whether spur or helical. Furthermore, actual micro-geometries depend on hob accuracies, which require their compensation. That yields equivalent images to those produced from simulated data.