摘要
搭建铣削力与工件表面粗糙度铣削试验系统,设计正交试验方案,对难加工金属TC4钛合金进行铣削试验。利用三向力测力仪采集铣削力信号,用表面粗糙度测定仪测量工件表面粗糙度,并提取特征值。对特征值进行3种正态性检验。研究结果表明,特征值均不满足严格的正态分布。基于Spearman相关性分析,得到三向铣削力与表面粗糙度相关系数均介于0.7~0.8之间,属于极强相关,可以用于构建表面粗糙度预测模型。基于响应面法,分别以铣削工艺参数、铣削力及铣削工艺参数-铣削力组合为连续因子,以表面粗糙度为响应因子做响应面分析,建立了3种表面粗糙度预测模型。通过对模型参数、表面粗糙度拟合值与实测值的对比曲线及残差散点图的分析可知,铣削工艺参数-铣削力组合预测模型的预测精度最高,多元相关系数值为0.9173,修正的多元相关系数值为0.8552,远高于其他2种模型,可以较好地预测TC4钛合金的表面粗糙度,证明了采用多因子组合的方法提高模型精度的可行性,提供了一种可靠的提高钛合金铣削表面粗糙度预测精度的方法。
The milling force and workpiece surface roughness milling experiment system was built,the orthogonal test scheme was designed,the milling test on the difficult machining metal TC4 titanium alloy was carried out.The milling force signals were collected by three-dimensional force dynamometer,the workpiece surface roughness was measured by surface roughness instrument,and the eigenvalue was extracted.Three normality tests were carried out on the eigenvalues,and the research result shows that the eigenvalues do not meet the strict normal distribution.Based on Spearman correlation analysis,the correlation coefficients between three-dimensional milling force and surface roughness are between 0.7 and 0.8,which belongs to strong correlation and can be used to build surface roughness prediction model.Based on the response surface method,three kinds of surface roughness prediction models were established with milling process parameters,milling force and the combination of milling process parameters and milling force as continuous factors and surface roughness as response factor.Through the analysis of the comparison curve and residual scatter diagram of model parameters,surface roughness fitting value and measured value,it was concluded that the prediction accuracy of the combined prediction model of milling process parameters and milling force is the highest,the value of multivariate correlation coefficient is 0.9173,and the value of modified multivariate correlation coefficient is 0.8552,which is much higher than the other two models.It can better predict the surface roughness of TC4 titanium alloy,prove the feasibility of the method of multi factor combination to improve the accuracy of the model,and provide a new method to improve the prediction accuracy of the surface roughness of TC4 titanium alloy milling.
作者
李远博
邵明辉
李顺才
王强
LI Yuanbo;SHAO Minghui;LI Shuncai;WANG Qiang(JSNU-SPBPU Institute of Engineering,Jiangsu Normal University,Xuzhou 221116,China;School of Mechanical and Electrical Engineering,Jiangsu Normal University,Xuzhou 221116,China;Xuzhou Changhang Technology Co.,Ltd.,Xuzhou 221116,China)
出处
《现代制造工程》
CSCD
北大核心
2023年第3期96-104,共9页
Modern Manufacturing Engineering
基金
国家自然科学基金项目(52075231)
国家级大学生实践创新训练计划项目(202110320033Z)
江苏省中外合作办学平台联合科研项目
江苏省高校实验室研究会立项资助研究课题项目(GS2022YB15)
徐州市科技计划项目(KC20188)
江苏师范大学本科教育教学课题项目(JYKTZ202101)。
关键词
TC4钛合金
铣削力
表面粗糙度
相关性分析
响应面法
组合预测模型
TC4 titanium alloy
milling force
surface roughness
correlation analysis
response surface method
combined prediction model
作者简介
李远博,本科生,研究方向为机械振动。E-mail:3155642980@qq.com;通信作者:邵明辉,硕士,高级实验师,研究方向为数控技术与快速成型制造。E-mail:db@jsnu.edu.cn。
