Face gear generating grinding residual model based on the normal cutting depth iterative method

The generating grinding method has the characteristics of high machining accuracy and high efficiency. Therefore, it is widely used in the finishing process of the face gear tooth surface. The physical performance of the grinding process is an important factor that influences the machining accuracy of the face gear tooth surface. Therefore, to improve the manufacturing accuracy of the face gear, it is necessary to accurately simulate the grinding process of the face gear. Owing to the influence of the complex spatial geometric characteristics of the face gear tooth surface and the grinding wheel, establishing a surface residual modeling method for the face gear is a key challenge in simulating the generating grinding process. To address the aforementioned issues, this study proposes a normal cutting depth iterative method to calculate the grinding residual in the face gear generating grinding process. This method considers the complex 3D spatial characteristics of the face gear tooth surface and establishes the spatial residual model of each node of the face gear surface in the process of generating grinding. Compared with other residual algorithms based on 2D truncation or Boolean operation of face gears, this algorithm has higher computational accuracy and efficiency. Thus, it lays the foundation for accurately establishing the complex spatial microscopic surface topography in the face gear generating grinding process.

Automated summary

The generating grinding method is widely used in finishing the face gear tooth surface due to its high machining accuracy and efficiency. The physical performance of the grinding process greatly affects the machining accuracy, so accurately simulating the grinding process is necessary for improving the manufacturing accuracy of the face gear. This study proposes a normal cutting depth iterative method to calculate the grinding residual in the face gear generating grinding process, which considers the complex 3D spatial characteristics of the face gear tooth surface and achieves higher computational accuracy and efficiency compared to other methods.