CMQL和UEV下Incone1690镍基合金切削力和温度分析
Cutting Force and Temperature Analysis of Incone1690 Nickel-Base Alloy Under CMQL and UEV
王娟 1李峰 2王国强3
作者信息
- 1. 扬州工业职业技术学院智能制造学院,江苏 扬州 225000
- 2. 扬州大学机械工程学院,江苏 扬州 225009
- 3. 扬州天健机械制造有限公司,江苏 扬州 225009
- 折叠
摘要
低温微量润滑(CMQL)可以使切削阶段达到更优的切削摩擦性能,提升刀具耐磨性能并获得更优减磨效果;超声椭圆振动(UEV)能够提高难加工合金的切削性能.在超声椭圆振动基础上设置低温微量润滑条件,并通过实验分析其切削效果.研究结果表明:采用UEV和CCMQL模式时,切削力与进给力降低比例依次为62.3%与75.0%,在低温微量润滑过程能够实现更低温度控制效果,有效防止工件的热软化问题.普通切削时温度都接近445℃.UEV可以获得更低温度,同时运用UEV和CMQL处理方式,温度下降了16.1%,采用UEV和CCMQL方式时,温度下降19.5%.通过降低喷嘴角度的方式,获得更低切削雾滴温度,刀尖应力出现降低实现刀具保护,促进刀具寿命延长.该研究有助于改善合金切削工艺,对提高切削质量提供了一个思路.
Abstract
Low temperature micro-lubrication(CMQL)can achieve better cutting friction performance,improve tool wear resis-tance and obtain better wear reduction effect.Ultrasonic elliptic vibration(UEV)can improve the machinability of difficult-to-work alloys.On the basis of ultrasonic elliptic vibration,low temperature micro-lubrication conditions were set,and the cutting effect was analyzed by experiments The results show that when UEV and CCMQL modes are adopted,the reduction ratio of cut-ting force and feeding force is 62.3%and 75.0%respectively,which can realize lower temperature control effect in the process of low temperature micro-lubrication,and effectively prevent the thermal softening problem of workpiece.Common cutting tempera-ture is close to445℃.Lower temperature can be obtained by UEV.At the same time,the temperature decreases by 16.1%when us-ing UEVand CMQL,while the temperature decreases by 19.5%when using UEVand CCMQL.By reducing the Angle of the noz-zle,lower cutting fog drop temperature is obtained,and the stress of the tool tip is reduced to realize tool protection and promote the extension of tool life.The research is helpful to improve alloy cutting technology and provides a way to improve cutting quality.
关键词
超声椭圆振动/低温微量润滑/切削/镍基合金/性能分析Key words
Ultrasonic Elliptic Vibration/Low Temperature Trace Lubrication/Cutting/Nickel Base Alloy/Per-formance Analysis引用本文复制引用
基金项目
江苏省现代教育技术研究项目(2019-R-769922)
出版年
2024
NETL
NSTL
万方数据