Simulation of grinding process for cemented carbide based on an integrated process-machine model

Feng, Wei; Yao, Bin; Yu, Xiaojin; Sun, Weifang; Xiao, Cao

doi:10.1007/s00170-016-9035-0

Cited by 6 publications

(1 citation statement)

References 14 publications

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“…Hence, it is widely used in the nishing process of face gears. To improve the generating grinding manufacturing accuracy of face gears, it is necessary to accurately simulate the grinding process [2] . And the dynamic physical parameters are the crucial factors of the grinding process.…”

Section: Introductionmentioning

confidence: 99%

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

Cai

Yao

et al. 2023

Int J Adv Manuf Technol

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The generating grinding method has the characteristics of high machining accuracy and high e ciency. Therefore, it is widely used in the nishing process of the face gear tooth surface. The physical performance of the grinding process is an important factor that in uences 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 in uence 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. First, the mathematical models of the grinding wheel pro le, grinding trajectory, and face gear tooth surface are established. Then, the initial conditions of algorithm iteration are established by calculating the parameter coordinates of the meshing point of the grinding wheel and the face gear at each moment. Subsequently, the tooth surface of the face gear is gridded, its node normal direction is determined, and the normal grinding residual matrix of the face gear tooth surface is obtained. Based on the cutting surface of the grinding wheel, the normal cutting depth of the grid node on the tooth surface at each moment is iteratively approximated. Furthermore, by taking the meshing point as the initial solution point and the face gear normal grinding residual matrix at the previous moment as the initial condition of iteration, grinding area on the face gear tooth surface at the current moment is calculated, and the face gear normal grinding residual matrix is updated. Finally, these steps are repeated until the grinding is completed, and the nal normal grinding residual matrix of the face gear is obtained. Thus, the generating grinding residual of the face gear is obtained. This algorithm considers the complex 3D spatial characteristics of the face gear tooth surface and establishes the spatial residual model of each grid node of the face gear 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 e ciency. Thus, it lays the foundation for accurately establishing the complex spatial microscopic surface topography in the face gear generating grinding process.

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Section: Introductionmentioning

confidence: 99%