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

doi:10.3901/JME.2018.21.221

机械工程学报 ›› 2018, Vol. 54 ›› Issue (21): 221-230.doi: 10.3901/JME.2018.21.221

扫码分享

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

王艳¹, 李德蔺¹, 刘建国¹, 宋红林², 彭水平¹, 汪锐¹

1. 上海理工大学机械工程学院上海 200093;
2. 江苏三菱磨料磨具有限公司盐城 224000

收稿日期:2017-11-04 修回日期:2018-04-21 出版日期:2018-11-05 发布日期:2018-11-05
通讯作者: 王艳(通信作者),女,1969年出生,博士,教授。主要研究方向为精密磨削加工与特种加工。E-mail:yanwang909909@163.com
作者简介:李德蔺,男,1993年出生,硕士。主要研究方向为精密磨削加工。E-mail:18116349010@163.com;刘建国,男,1962年出生,高级工程师。主要研究方向为精密磨削工艺。E-mail:liujianguo@usst.edu.cn;宋红林,男,1967年出生,高级工程师。主要研究方向为磨削加工。E-mail:shl@sanlingcn.com;彭水平,男,1990年出生,硕士。主要研究方向为精密磨削加工。E-mail:15901876956@163.com;汪锐,男,1995年出生,硕士。主要研究方向为精密磨削加工。E-mail:18301926280@163.com
基金资助:
2015江苏省重点研发计划资助项目（BE2015139）。

Prediction and Experimental Verification of Workpiece Surface Topology in Axial Ultrasonic Vibration Assisted Grinding Based on Dynamic Profile Sampling Method

WANG Yan¹, LI Delin¹, LIU Jianguo¹, SONG Honglin², PENG Shuiping¹, WANG Rui¹

1. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093;
2. Jiangsu Mitsubishi Abrasives Co., Ltd., Yancheng 224000

Received:2017-11-04 Revised:2018-04-21 Online:2018-11-05 Published:2018-11-05

摘要/Abstract

摘要： 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。

关键词: 动态轮廓采样法, 工件表面形貌, 预测, 轴向超声振动辅助磨削

Abstract: 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.

Key words: axial ultrasonic vibration assisted grinding, dynamic profile sampling method, prediction, workpiece surface topology

中图分类号:

TG580

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.

WANG Yan, LI Delin, LIU Jianguo, SONG Honglin, PENG Shuiping, WANG Rui. Prediction and Experimental Verification of Workpiece Surface Topology in Axial Ultrasonic Vibration Assisted Grinding Based on Dynamic Profile Sampling Method[J]. Journal of Mechanical Engineering, 2018, 54(21): 221-230.

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: http://www.cjmenet.com.cn/CN/10.3901/JME.2018.21.221

http://www.cjmenet.com.cn/CN/Y2018/V54/I21/221

参考文献

[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.

[1]	冯小明, 万珍平, 孙东升, 龙元香. 制动器增力传动机构研究现状与发展趋势[J]. 机械工程学报, 2026, 62(8): 1-20.
[2]	皮大伟, 李绪航, 张宸硕, 严永俊, 王洪亮, 王显会. 瞬时转向中心求解下的角模块车辆路径跟踪预测控制[J]. 机械工程学报, 2026, 62(8): 450-461.
[3]	罗忠, 李洪雨, 李雷, 董胤哲. 数字孪生驱动的转子系统变工况难测点响应实时预测方法[J]. 机械工程学报, 2026, 62(7): 221-233.
[4]	原源, 刘秀成, 祁攀, 曹文博, 王峥鸿, 吴斌, 高翔. 基于磁致伸缩SH₀导波传感器换能曲线的表面硬度无损检测方法[J]. 机械工程学报, 2026, 62(6): 228-236.
[5]	刘辉, 马小康, 韩立金, 项昌乐. 基于深度强化学习和模型预测控制的混合动力电动汽车实时能量管理策略[J]. 机械工程学报, 2026, 62(6): 302-313.
[6]	石逸涵, 张旭, 庄存波, 刘金山, 王家修, 孙连胜. 集成双重注意力机制CNN-LSTM时空网络的离散车间生产瓶颈预测[J]. 机械工程学报, 2026, 62(5): 49-60.
[7]	唐小林, 张焜埸, 陈止戈, 杨剑英, 杨为. 融合交互预测信息的自动驾驶安全规划方法研究[J]. 机械工程学报, 2026, 62(4): 249-262.
[8]	杨亮亮, 龚壮壮, 何西旺, 王沐晨, 闵强, 阚子云, 宋学官. 面向结构损伤识别的数字孪生建模[J]. 机械工程学报, 2026, 62(4): 328-341.
[9]	詹远新, 林勤龙, 刘洋, 高英, 吴剑明, 张嘉振. 面向Ti-6Al-4V合金增材制造的机器学习研究进展[J]. 机械工程学报, 2026, 62(3): 86-103.
[10]	李雨露, 李俊峰, 万章艺, 魏正英. 基于YOLO算法的激光粉末床熔融成形层形貌分类识别与成形质量预测研究[J]. 机械工程学报, 2026, 62(3): 218-234.
[11]	王维民, 刘延振. 基于叶尖监测的航空发动机故障诊断与预警技术研究综述[J]. 机械工程学报, 2026, 62(2): 1-16.
[12]	高书颜, 黄明坤, 黄涛, 张小明, 丁汉. 五轴加工通用刀具切触区高效计算及切削力预测[J]. 机械工程学报, 2025, 61(9): 132-141.
[13]	赵礼辉, 王震, 刘天胤, 刘东俭, 翁硕, 张东东. 随机载荷下电驱动系统高速球轴承疲劳寿命预测方法研究[J]. 机械工程学报, 2025, 61(8): 214-227.
[14]	肖钊, 曹智辉, 邓杰文, 段书用, 赵前程, 戴巨川, 陶洁. 融合光纤传感数据与门控循环神经网络的风电机组载荷预测方法[J]. 机械工程学报, 2025, 61(8): 272-282.
[15]	朱海华, 付泰然, 李霏, 刘长春, 蔡祺祥, 唐敦兵. 基于数字孪生的复杂产品装配物料过程齐套时间预测[J]. 机械工程学报, 2025, 61(8): 384-398.

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

Prediction and Experimental Verification of Workpiece Surface Topology in Axial Ultrasonic Vibration Assisted Grinding Based on Dynamic Profile Sampling Method

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价