Research on Load Prediction Method of Aviation Herringbone Gear Dislocation Grinding

The gears of helicopter transmission system have strict requirements on machining accuracy, and the accurate prediction of tooth surface grinding force is the key to its manufacturing. The existing model simplifies the micro-contact behavior of the abrasive-workpiece, which limits the accuracy of the grinding load solution. In this paper, the stress state of single abrasive grain at different stages is refined from the micro level, and the grinding force mechanism model of contact area superposition is established. A mechanism-constrained data-driven grinding force prediction algorithm (MCDDP) is proposed. The algorithm integrates the microscopic force mechanism as a physical constraint into the neural network. The experimental results show that the R2 of the model for predicting the normal and tangential grinding forces under multiple working conditions is higher than 0.98, and the average error is reduced by about 17% compared with the traditional model. This study reveals the non-uniform force mechanism of abrasive-workpiece, realizes the integration of mechanism model and data-driven method, and provides engineering theoretical and technical support for grinding force prediction and process parameter optimization of aviation precision gears.