摘要
基于灰色理论模型突出的非线性拟合功能,本文通过数据的关联度分析法建立动态模型,即可近似任意非线性函数。灰色理论兼备自主学习与建模响应敏捷等特点,针对轮槽铣床主轴箱热误差模型数据处理复杂的特性,具有良好的适用性。结果表明,通过建立灰色理论热误差预测模型,以主轴箱关键温度测点所测温度为依据,确定分析模型的理论输出与系统实际热特性关联度,最终得到预测结果鲁棒性较好的主轴箱热误差。
Based on the prominent nonlinear fitting function of gray theory model,this paper established a dynamic model through data correlation analysis,which could approximate any nonlinear function.Grey theory combines the characteristics of independent learning and quick modeling response,which has good applicability for the complex data processing characteristics of the thermal error model of the wheel groove milling machine headstock.The results show that by establishing a gray theoretical thermal error prediction model,based on the temperature measured at the key temperature measurement points of the headstock,the correlation between the theoretical output of the analysis model and the actual thermal characteristics of the system is determined,finally,the thermal error of the headstock box with better robustness is obtained.
作者
李志伟
LI Zhiwei(Sichuan College of Architectural Technology,Deyang Sichuan 618000)
出处
《河南科技》
2020年第34期21-24,共4页
Henan Science and Technology
基金
国家重大科技专项(2011ZX04002-081)。
关键词
主轴箱
灰色理论
瞬态环境
温度测点
热误差分析
headstock
grey theory
transient environment
temperature measuring point
thermal error analysis
