基于逐步回归的机床温度测点优化及热误差建模技术

发布时间：2018-01-23 07:45

本文关键词： 数控机床逐步回归温度测点优化热误差建模　出处：《制造技术与机床》2015年12期 　论文类型：期刊论文

【摘要】：通过对机床温度测点进行优化,建立其与机床热误差之间的数学模型,对机床热误差进行实时预测与补偿控制,是提高数控机床加工精度的重要途径。为解决现有机床热误差模型预测精度低、鲁棒性差的问题,提出一种基于逐步回归的数控机床温度测点优化方法。通过偏F统计量的检验,在初步建立的回归模型中逐个引入新变量,剔除不显著的老变量,实现温度测点的优化布置,获得数控机床热误差的最优回归模型。将该方法应用于某数控机床,结果表明,基于逐步回归的机床热误差模型,所用温度变量最少,且预测精度最高。
[Abstract]:By optimizing the temperature measuring points of the machine tool, the mathematical model between the temperature measurement point and the thermal error of the machine tool is established, and the real-time prediction and compensation control of the thermal error of the machine tool are carried out. It is an important way to improve the machining accuracy of NC machine tools. In order to solve the problem of low prediction accuracy and poor robustness of the existing thermal error model of machine tools. This paper presents an optimization method for temperature measurement points of NC machine tools based on stepwise regression. Through the test of bias F statistics, new variables are introduced one by one in the initial regression model, and the old variables that are not significant are eliminated. The optimal regression model of thermal error of NC machine tools is obtained by optimizing the layout of temperature measuring points. The results show that the thermal error model based on stepwise regression is applied to a numerical control machine tool. The temperature variables used are the least and the prediction accuracy is the highest.
【作者单位】：山东理工大学机械工程学院;
【基金】：国家自然科学基金项目(51305244)
【分类号】：TG659
【正文快照】： 数控机床热误差补偿技术是提高机床加工精度的重要途径,而热误差建模则是实施补偿的前提和基础,高精度、高鲁棒性的热误差模型是提高制造业水平和国际竞争力的关键所在。为解决这一问题,各国研究人员进行了不懈努力[1-5]。与神经网络[6]、遗传算法[7]等人工智能模型相比,基于

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