摘要
热误差严重影响机床的加工精度,通过热误差补偿技术提高机床加工精度是一个非常有效的途径。温度测点的选择与优化是热误差补偿技术研究中的难点。为了合理地减少温度测点数量,通过实验检测不同工况下进给系统各部件的温度分布,利用模糊聚类分析方法按温度变化规律对温度测点进行分类,通过对主轴温度场分布情况的分析,利用相关性分析方法,从24个温度测点中选取5个温度特征点,用于加工中心的热误差补偿,很大程度上提高了热误差建模的效率。结合以上两种方法,优化温度传感器测点的布置位置,减少了温度测点数量,提高了热误差补偿的精度。
Thermal error seriously affects the machining accuracy of the machine, and it is a very effective way to improve the precision of the machine by thermal error compensation. The selection and optimization of temperature measurement points are the difficulties in the study of thermal error compensation technology. In order to reduce the number of temperature measurement points reasonably, the temperature distribution of the parts in the feed system under different working conditions is tested. The fuzzy clustering analysis method is used to classify the temperature variation of the measuring point. Through the analysis of the distribution of the spindle temperature field and the method of correlation analysis, five temperature characteristic points were selected from 24 temperature measuring points to compensate the thermal error of the machining center ,which greatly improved the efficiency of the thermal error model. The method optimizes the position of the temperature sensor measuring point, achieves the simplified temperature measuring point and improves the accuracy of the thermal error compensation.
作者
沈振辉
杨拴强
Shen Zhenhui;Yang Shuanqiang(Fujian Jiangxia University,Fuzhou 350108,China)
出处
《现代制造工程》
CSCD
北大核心
2018年第11期112-118,共7页
Modern Manufacturing Engineering
关键词
温度测点
模糊聚类分析
相关性分析
热误差模型
热误差补偿
temperature measuring points
fuzzy clustering analysis
correlation analysis
thermal error model
thermal error compensation
