Prediction of load sharing based HCR spur gear stresses and critical loading points using artificial neural networks

摘要

The prediction of the load shared by a pair of teeth, maximum contact and fillet stresses and the respective location of the critical loading point becomes rather a difficult task in High Contact Ratio (HCR) gears as the contact ratio exceeds two. As this prediction greatly depends on the gear parameters like pressure angle, addendum factor and teeth number, an attempt has been made to work on this area highlighting these aspects using Finite Element (FE) Multi Pair Contact Model (MPCM). The minimum value of contact ratio under consideration is 2.1. However, the maximum is chosen as 2.9. A new methodology based on Artificial Neural Networks (ANNs) is proposed for the prediction of Load-Sharing Ratio (LSR), maximum fillet and contact stresses and the respective critical loading points. The data set generated from the MPCM has been used to train the networks and, furthermore, its effectiveness is proved by a different data set of HCR gear pairs determined for the randomly selected parameters.