摘要
目前的高温合金刀具磨损在线预测方法预测时间过长,预测误差较大。为了解决上述问题,基于高斯回归分析方法建立了一种新的高温合金刀具磨损在线预测方法,设立高斯回归模型,分析切削力和刀具磨损时间序列数据,对数据进行划分,列出线性数据和非线性数据。引入平滑度理论对建立的高斯回归模型进行优化,借助构建的高斯回归模型计算刀具刀面的最大磨损宽度值。选择平滑度较高的核函数进行模型优化,对刀具的使用状态进行实时观测,精准估计磨损量,实现在线预测。实验结果表明,基于高斯回归分析的高温合金刀具磨损在线预测方法能够有效缩短预测时间,降低预测误差。
The time of online prediction method of high-temperature alloy tool wear is too long,which leads to large prediction errors.In order to solve the above problems,a new online prediction method for high-temperature alloy tool wear is established based on the Gaussian regression analysis method.A Gaussian regression model is set up to analyze the cutting force and tool wear time series data.Linear data and nonlinear data are listed by data classification,smoothness theory is introduced to optimize the established Gaussian regression model.By constructed Gaussian regression model the maximum wear width of the tool face is calculated.A kernel function with higher smoothness is used to optimize the model.The use state of the tool is observed in real time.By accurate estimation of the amount of tool wear,the online prediction is achieved.The experimental results show that the online prediction method of high-temperature alloy tool wear based on Gaussian regression analysis can effectively shorten the prediction time and reduce the prediction error.
作者
王爽
Wang Shuang(Jilin Railuway Technology College,Jilin,Jilin 132000,China)
出处
《工具技术》
北大核心
2022年第2期83-87,共5页
Tool Engineering
关键词
高斯回归分析
高温合金刀具
刀具磨损
在线预测
Gaussian regression analysis
high-temperature alloy tools
tool wear
online prediction
