Analysis of Heat Treat Growth on Carburized Ring Gear and Multivariate Regression Model Development Free

Accurate assessment of heat treat (HT) growth on carburized ring gears is of critical importance when developing new gears or implementing various design/process changes on current production gears. The traditional approach has been to conduct expensive and time consuming HT trials with green and after- HT measurements on a case-by-case basis. An advancement of this process was to create an extensive database in order to develop a predictive model. Various statistical analyses were performed using Minitab. Ring gear HT growth on measurements between pins expressed in % growth gave better predicting power than delta (mm) growth. The best subset model with green hardness data utilizes 7 factors (material, key geometrical features) and yields 98.3% R². The model developed from a larger dataset without green hardness yields 89.8% R². On-going work includes continuously updating the database and refining the model. This work will help minimize the number of trials needed for new product launches and shortening of the development cycle.