航空学报 > 2014, Vol. 35 Issue (12): 3489-3498   doi: 10.7527/S1000-6893.2014.0065

基于多颗磨粒随机分布的虚拟砂轮建模及磨削力预测

张祥雷, 姚斌, 冯伟, 沈志煌   

  1. 厦门大学 物理与机电工程学院, 厦门 361005
  • 收稿日期:2014-02-26 修回日期:2014-03-28 出版日期:2014-12-25 发布日期:2014-04-22
  • 通讯作者: 姚斌 男, 博士, 教授, 博士生导师.主要研究方向: 复杂曲面成形与先进制造技术. Tel: 0592-2186923 E-mail: yaobin@xmu.edu.cn E-mail:yaobin@xmu.edu.cn
  • 作者简介:张祥雷 男, 博士研究生.主要研究方向: 先进制造技术. Tel: 0592-2186923 E-mail: zhxile2008@sina.com
  • 基金资助:

    厦门市科技计划项目 (3502Z20131007)

Modeling of Virtual Grinding Wheel Based on Random Distribution of Multi Abrasive Grains and Prediction of Grinding Force

ZHANG Xianglei, YAO Bin, FENG Wei, SHEN Zhihuang   

  1. Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361005, China
  • Received:2014-02-26 Revised:2014-03-28 Online:2014-12-25 Published:2014-04-22
  • Supported by:

    Science and Technology Plan Projects of Xiamen City (3502Z20131007)

摘要:

利用VHX-600E型超景深显微镜测量了金刚石砂轮表面的磨粒分布情况,计算得到了砂轮表面的磨粒密度、真实接触弧长以及砂轮总的磨粒数和有效磨粒数.基于磨粒间隔分布假设和虚拟格子方法在虚拟砂轮端面随机分布等磨粒密度的多颗正六面体磨粒,并随机分配磨粒的位姿以模拟砂轮的真实形貌.将1/4虚拟砂轮模型导入Deform-3D软件中,建立三维虚拟磨削仿真模型,采用Lagrangian Incremental算法获得多颗磨粒的仿真磨削力值,并建立了基于多颗磨粒磨削仿真的磨削力预测模型.通过金刚石砂轮端面磨削硬质合金刀片的实验,比较了实测磨削力与预测磨削力;仿真与实验结果具有一致性,证明了采用本方法建立的多颗磨粒虚拟磨削仿真模型可以用于磨削力预测,为多颗磨粒共同磨削的磨削力研究提供了新的思路.

关键词: 多颗磨粒, 随机分布, 砂轮, 磨削力, 端面磨削

Abstract:

The distribution of abrasive grains existing in diamond grinding wheel surface is firstly measured by a VHX-600E optic microscope. The density of abrasive grains, the actual contact length and the effective number of abrasive grains are then calculated. Based on the assumption of interval distribution of abrasive grains and virtual grid method, the surface of virtual grinding wheel is randomly distributed with multi hexahedron abrasive grains which are equal in density. Besides, the posture of abrasive grains is randomly allocated to simulate the real topography of grinding wheel. The 3D simulated model of virtual grinding is built by importing a 1/4 virtual grinding wheel model into Deform-3D software and the simulated grinding force value of multi grains is obtained by Lagrangian Incremental algorithm. The multi-grain grinding force predictive model is then built with the simulated model. A carbide blade grinding experiment is performed to validate the predictive model by comparing the measured grinding force with the predictive force. The test verifies the accuracy and effectiveness of the proposed model in the paper. This paper provides a new method to investigate the grinding force which is co-grinded by multi-grains.

Key words: multi abrasive grains, random distribution, grinding wheels, grinding force, face grinding

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