Reduction of edge effect using response surface methodology and artificial neural network modeling of a spur gear treated by induction with flux concentrators

Abstract

The aim of the study is to determine the effect of each parameter involved in the induction heating process on the final temperature distribution and case depth dispersion of a spur gear placed between two other gears having identical shapes and acting as a flux concentrator using two different approaches. The purpose of flux concentrators is to adjust the heat distribution in the part at the end of the heating process and to produce a better case depth between the middle and the edge of the gear. Mechanical properties of the gear could be improved by minimizing the edge effect at the tooth; thus, the optimization of temperature gradient between the middle and the edge plan by varying geometrical and machine parameters was studied. Two structured and comprehensive approaches to design an efficient model based on analysis of variance (ANOVA) and artificial neural networks (ANN) for the estimation of quality and the prediction of temperature profiles and edge effect was developed. The obtained results demonstrate that the statistical model was able to predict accurately the behavior of temperature and case depth distribution. In the final phase, several experimental tests were conducted on the induction machine to validate the simulation results and the prediction model.