Modeling Analysis of Grinding Process Driven by Single Grain Grinding Mechanism and Data Fusion

doi:10.3901/JME.2023.07.200

Abstract

Abstract: Accurate prediction of the grinding process is of great significance to achieve the goal of energy conservation and emission reduction in China. In view of the problems that the existing grinding energy consumption research cannot accurately characterize the grinding energy flow and the dynamic change data of energy consumption are not considered, a modelling analysis method of grinding process driven by single grain grinding mechanism and data fusion is proposed. The surface topography model of the grinding wheel considering the size, position, angle, and edge height of the grain is established. The mathematical expression model of the contact analysis between grains and workpiece material is deduced, and the establishment method of grinding force and energy consumption based on different abrasive grain shapes is discussed. On this basis, a dynamic self-learning and energy consumption prediction model integrating grinding mechanism and data analysis is established. The experimental results show that the average relative error of the fusion model is 3.630 7%, which can not only reveal the generation and evolution mechanism of energy in the grinding process, but also achieve accurate prediction of the grinding results.

Key words: single grain, grinding, mechanism and data fusion, grinding force, grinding energy consumption

CLC Number:

TG156

Lü Lishu, DENG Zhaohui, YUE Wenhui, WAN Linlin, LIU Tao. Modeling Analysis of Grinding Process Driven by Single Grain Grinding Mechanism and Data Fusion[J]. Journal of Mechanical Engineering, 2023, 59(7): 200-215.

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