单颗磨粒磨削机理与数据融合驱动的磨削过程建模分析

实现磨削过程的精准预测对于实现我国节能减排的目标具有重要意义。针对现有磨削能耗研究无法准确表征出磨削能量流动情况和未考虑能耗动态变化数据等问题，提出一种基于单颗磨粒磨削机理与数据融合驱动的磨削过程建模分析方法。建立了考虑磨粒的尺寸、位置、角度、出刃高度的砂轮表面形貌模型，推导了磨粒与工件材料接触分析情况的数学表述模型，探讨了基于不同磨粒形状的磨削力与能耗模型的建立方法；在此基础上，建立了零件磨削机理与数据分析相融合的动态自学习能耗预测模型。实验结果表明融合模型的相对误差平均值为3.630 7%，不仅可以揭示磨削过程能量的生成和演变机制，更能够实现对磨削结果的精准预测。

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

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.