基于贝叶斯神经网络的机床热误差建模

热误差严重影响着机床的加工精度,对机床关键部件进行热特性分析是开发精密机床的重要环节。通过测量包括数控机床的特殊位置温度和定位误差在内的热特性,研究了温升与定位误差之间的关系,提出了一种基于贝叶斯神经网络的热误差建模方法。通过K-means聚类和相关系数法来选择温度敏感点,可以有效地抑制温度测量点之间的多重共线性问题。结果表明:通过使用贝叶斯神经网络能提高机床88.0159%的精度,比BP神经网络高出15.7638%,与BP神经网络模型相比,贝叶斯神经网络具有更加优良预测性能。贝叶斯神经网络模型为降低机床热误差的影响提供了新思路。

Research on thermal error modeling of machine tool based on bayesian neural network

Thermal error seriously affects the machining accuracy of the machine tool.The thermal characteristic analysis of the key parts of the machine tool is an important link in the development of precision machine tool.Therefore,this paper studies the relationship between temperature rise and positioning error by measuring the thermal characteristics including the temperature and positioning error of special position of CNC machine tool,and proposes a thermal error modeling method based on Bayesian neural network.Using K-means clustering and correlation coefficient method to select temperature sensitive points can effectively suppress multicollinearity between temperature measurement points.The results show that the accuracy of machine tool can be improved by 88.0159%by using bayesian neural network,which is 15.7638%higher than BP neural network.Compared with BP neural network model,bayesian neural network has better prediction performance.Bayesian neural network model provides a new idea to reduce the influence of thermal error of machine tool.