CLC number:
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2024-04-16
Cited: 0
Clicked: 1395
Citations: Bibtex RefMan EndNote GB/T7714
Yanqin LI, Daohui XIANG, Guofu GAO, Feng JIAO, Bo ZHAO. Prediction of undeformed chip thickness distribution and surface roughness in ultrasonic vibration grinding of inner hole of bearings[J]. Journal of Zhejiang University Science A,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.A2200609 @article{title="Prediction of undeformed chip thickness distribution and surface roughness in ultrasonic vibration grinding of inner hole of bearings", %0 Journal Article TY - JOUR
轴承内孔超声振动磨削未变形切屑厚度分布及表面粗糙度预测机构:河南理工大学,机械与动力工程学院,中国焦作,454000 目的:超声振动辅助磨削在材料去除机理上与传统磨削有明显不同。超声振动辅助磨削中颗粒与工件相互作用的随机性会产生不同形状与厚度的切屑,然而,对超声磨削过程中的磨粒-工件相互作用的随机性及与表面粗糙度的关系还有待进一步研究。 创新点:提供一种快速有效的方法预测纵向超声振动磨削加工工件的表面粗糙度。 方法:1.采用符号差分法(表1)计算磨粒与工件的干涉深度和宽度,得到未变形切屑厚度(UCT)分布来描述磨粒与工件相互作用的随机特性。2.分析不同磨削参数下UCT厚度分布的特征。3.基于UCT分布,建立表面粗糙度预测模型。4.通过实验验证模型的有效性。 结论:1.基于超声磨削过程中磨粒的运动轨迹,提出符号差分法计算磨粒与工件的干涉深度和宽度,提高仿真效率。2.在超声磨削过程中UCT分布服从指数分布;通过提取不同加工参数下UCT分布的特征参数,发现超声波的应用可以改变UCT的深度和宽度,增强重复干涉效应,以及降低表面粗糙度。3.分析磨削参数对UCT分布特性的影响规律发现,基于UCT均值建立表面粗糙度预测模型得到的实验值与理论值的变化趋势一致,且最大偏差为14.3%。4.高砂轮转速、低工件速度、小磨削深度及合适的超声振幅有利于获得较低的UCT,从而形成较光滑的工件表面。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
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