基于多元自回归模型的电主轴热误差建模与预测

雷春丽; 芮执元

基于多元自回归模型的电主轴热误差建模与预测

雷春丽^1,2,
芮执元^1,2

1.
兰州理工大学数字制造技术与应用省部共建教育部重点实验室, 兰州 730050;
2.
兰州理工大学机电工程学院, 兰州 730050

基金项目:

国家科技重大专项项目(2009ZX04001-015)

甘肃省自然科学基金项目(1010RJZA043)资助

详细信息

作者简介:
雷春丽(1977-)，讲师，博士研究生，研究方向为精密加工与测试技术和复杂机械产品优化设计，lclyq2004@163.com;芮执元(联系人)，教授，博士生导师，Zhiyrui@163.com

雷春丽(1977-)，讲师，博士研究生，研究方向为精密加工与测试技术和复杂机械产品优化设计，lclyq2004@163.com;芮执元(联系人)，教授，博士生导师，Zhiyrui@163.com

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出版历程
- 收稿日期: 2011-08-03
- 刊出日期: 2015-06-10

Thermal Error Modeling and Forecasting Based on Multivariate Autoregressive Model for Motorized Spindle

Lei Chun-li^1,2,
Rui Zhi-yuan^1,2

1.
Key Laboratory of Digital Manufacturing Technology and Application, The Ministry of Education, Lanzhou University of Technology, Lanzhou 730050;
2.
School of Mechanical and Electronic Engineering, Lanzhou University of Technology, Lanzhou 730050

摘要

摘要: 为了减少电主轴的热误差,提高数控机床的加工精度,考虑热变形不仅与自身若干期的滞后值有关,还与当前温升及其滞后值有关,提出采用多元自回归方法建立电主轴热误差模型。首先将观测序列进行差分处理,剔除线性趋势项,然后利用Akaike判据获得自回归模型的阶数,用最小二乘法求得自回归模型的系数,最后用建立的自回归模型预测电主轴热误差,并通过试验验证该模型的有效性。试验结果表明基于位移的热误差自回归模型比基于温度的热误差多元线性回归模型有更好的精度。
- 热误差 /
- 多元自回归模型 /
- 电主轴 /
- 预测
Abstract: In order to reduce the thermal error of the motorized spindle and improve the manufacturing accuracy of NC machine tool, the thermal error model based on multivariate autoregressive method is proposed. This model con-siders not only the relationship between thermal deformation and its lagged value but also the relationship between thermal deformation and the present and lagged value of temperature rise. The linear trends of observed series are eliminated by numerical difference. The order of multivariate autoregressive (MVAR) model is adopted by using Akaike information criterion. The coefficients of the MVAR model are determined by the least square method. The established MVAR model is then used to forecast the thermal error and the experiments have shown the validity and robustness of this model. The results indicate that the displacement-based thermal error autoregressive model has much better accuracy than the temperature-based multiple linear regression model.
- thermal error /
- multivariate autoregressive model /
- motorized spindle /
- forecasting

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参考文献(11)

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