提出了一种基于动态轮廓采样法的轴向超声振动辅助磨削的工件表面形貌预测方法。假设磨粒直径服从正态分布，磨粒位置服从随机分布，生成砂轮表面形貌的模型，从运动学角度建立了轴向超声振动辅助磨削过程中任意磨粒的轨迹方程，针对磨粒运动轨迹的特点，提出了动态轮廓采样方法。通过建立磨削沟槽变宽模型，引入了磨削弹性变形模型和塑性堆积模型，对动态轮廓采样方法进行了修正，最终得出工件表面形貌的预测结果。对预测结果进行了试验验证，对比分析了工件表面形貌的预测结果和实测结果，两者特征相似，且比较工件表面粗糙度的预测值和实测值平均误差为5.3%，从而验证了该预测方法的准确性。
王艳
,
李德蔺
,
刘建国
,
宋红林
,
彭水平
,
汪锐
. 基于动态轮廓采样法的轴向超声振动辅助磨削工件表面形貌预测与试验验证[J]. 机械工程学报, 2018
, 54(21)
: 221
-230
.
DOI: 10.3901/JME.2018.21.221
A novel predicting method which is based on the dynamic profile sampling method for workpiece surface topology in axial ultrasonic vibration assisted grinding is presented. The surface topology model of grinding wheel is generated by assumptions that the diameters of sphere particles are distributed normally, and the positions of particles are distributed randomly. The formula of arbitrary particle motion path in axial ultrasonic vibration assisted grinding is established in a kinematic view. The dynamic profile sampling method is presented aiming at the feature of particle motion path. Then this method is amended by establishing the grove-broadening model as well as introducing the elastic deformation model and plastic pile-up model. Then the final surface topology of workpiece is generated. An experimental verification is conducted for the predicted results. A comparing analysis shows that predicted and measured surface topology of workpiece are featured similarity. Additionally, the average error between the predicted and measured surface roughness of workpiece is 5.3%, which verifies the accuracy of the predicting method.
[1] DENKENA B,FRIEMUTH T,REICHSTEIN M. Potentials of different process kinematics in micro grinding[J]. Annals of the CIRP,2003,52(1):463-466.
[2] TAWAKOLI T,AZARHOUSHANG B. Ultrasonic assisted dry grinding of 42CrMo4[J].International Journal of Advanced Manufacturing Technology,2009,42:883-891.
[3] 肖敏. 轴向超声振动辅助磨削机理的研究[D].沈阳:东北大学,2012. XIAO Min. Study on mechanism of axial ultrasonic-assisted Grinding[D]. Shengyang:Northeastern University,2012.
[4] ZHOU X,XI F. Modeling and predicting surface roughness of the grinding process[J]. International Journal of Machine Tools and Manufacture,2002,42(8):969-977.
[5] NGUYEN T A,BUTLER D L. Simulation of precision grinding process,part 1:generation of the grinding wheel surface[J]. International Journal of Machine Tools and Manufacture,2005,45:1321-1328.
[6] NGUYEN T A,BUTLER D L. Simulation of precision grinding process,part 2:Interaction of the abrasive grain with the workpiece[J]. International Journal of Machine Tools and Manufacture,2005,45:1329-1336.
[7] 陈东祥,田延岭.超精密磨削加工表面形貌建模与仿真方法[J]. 机械工程学报,2010,46(13):186-191. CHEN Dongxiang,TIAN Yanling. Modeling and simulation methodology of the machined surface in ultra-precision grinding[J]. Journal of mechanical engineering,2010,46(13):186-191.
[8] LIU Y,ANDREW W. Investigation of different grain shapes and dressing to predict surface roughness in grinding using kinematic simulations[J]. Precision Engineering,2013,37(3). 758-764.
[9] WANG Sheng,LI Changhe,ZHANG Dongkun. Modeling the operation of a common grinding wheel with nanoparticle jet flow minimal quantity lubrication[J]. International Journal of Machine Tools and Manufacture,2014,74(5):835-850.
[10] 冯伟,陈彬强,蔡思捷,等. 考虑机床-磨削交互的工件表面形貌仿真[J].振动与冲击,2016,35(4):235-240. FENG Wei,CHEN Binqiang,CAI Sijie,et al. Simulation of surface topography considering process-machine interaction in grinding[J]. Journal of Vibration and Shook,2016,35(4):235-240.
[11] HOU ZB,KOMANDURI R. On the mechanics of the grinding process-Part 1:Stochastic nature of the grinding process[J]. International Journal of Machine Tools and Manufacture,2003,43(15):1579-1593.
