摘要
针对数控机床热误差建模具有小样本、数据离散的特点,提出一种综合灰色预测和最小二乘支持向量机的热误差在线组合建模方法。根据机床温度和热误差的实验数据,分别建立热误差的灰色模型和最小二乘支持向量机模型,并通过加权系数将两者进行组合。以提高热误差的实测值和组合模型预测值之间的灰色综合关联度为目标,对模型的加权系数进行优化。在一台高架桥式龙门加工中心上进行建模实验,结果表明数控机床热误差最优权系数组合建模方法精度高、泛化能力强,优于灰色预测、最小二乘支持向量机和多元线性回归3种建模方法。利用该方法构建的预测模型进行机床热误差在线补偿,可有效减小热误差对机床加工精度的影响。
Due to the modeling of CNC machine thermal error has characters of small sample and discrete data,the combined modeling method was presented by integrating grey forecast and least square-support vector machine.According to the experimental data of machine temperature and thermal error,a grey forecast model and a least square-support vector machine were built respectively,and then a combination model was established by using weight coefficients.Taking the increase of the synthetic grey correlation between experimental data and the combined model＇s forecast value as the aim,optimization of weight coefficients was done.A modeling test was designed on a viaduct gantry machining center,and the result showed that the optimal weights-based combined modeling was prior to grey forecast,least square-support vector machine and multiple linear regressions on accuracy and generalization.Application of the combined model on the online compensation for CNC machine thermal error can effectively reduce the influence of thermal error on machine＇s precision.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2012年第5期216-221,共6页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金资助项目(51075287)
国家重大科技专项资助项目(2010ZX04015-011)
关键词
数控机床
热误差
在线补偿
灰色预测
最小二乘支持向量机
灰色综合关联度
CNC machine
Thermal error
Online compensation
Grey forecast
Least square-support vector
Synthetic grey correlation
