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作 者:孙昂 刘茂龙 谢新连[2] 于贺春[3] SUN Ang;LIU Maolong;XIE Xinlian;YU Hechun(Ship and Ocean Engineering College,Dalian Maritime University,Dalian Liaoning 116026,China;Logistics Research Institute,Dalian Maritime University,Dalian Liaoning 116026,China;Zhongyuan University of Technology,Zhengzhou Henan 450007,China)
机构地区:[1]大连海事大学船舶与海洋工程学院,辽宁大连116026 [2]大连海事大学物流研究院,辽宁大连116026 [3]中原工学院,河南郑州450007
出 处:《机床与液压》2021年第6期118-123,共6页Machine Tool & Hydraulics
基 金:中央高校基本科研业务费专项资金资助项目(3132019303)。
摘 要:为提高数控车床主轴热误差的预测精度,以某型号数控车床主轴为研究对象,提出基于小波神经网络(WNN)的主轴热误差建模方法。利用K-means++聚类结合相关性分析理论,将温度测点从10个减小到2个。针对小波神经网络对初始值敏感的问题,采用蝙蝠算法(BA)将预测输出值与实验测量值之差的绝对值作为个体适应度函数,将蝙蝠个体的位置向量映射为小波神经网络的初始连接权值、尺度因子及平移因子,实现对小波神经网络初始值的优化。利用优化后的小波神经网络建立车床主轴热误差预测模型,与未优化的小波神经网络和BP神经网络预测模型对比。结果表明:BA-WNN对主轴轴向热误差的预测精度较高、残差较小、预测能力更强。In order to improve the prediction accuracy of the thermal error of the CNC lathe spindle,a CNC lathe spindle was taken as the research object,and a thermal error modeling method based on wavelet neural network(WNN)was proposed.The K-means++clustering and correlation analysis theory were used to determine the temperature measuring points from 10 to 2.Aimed at the problem that the WNN was sensitive to the initial value,the bat algorithm(BA)was used to calculate the absolute value of the difference between the predicted output value and the experimental measurement value as the individual fitness function,and the position vector of the bat individual was mapped to the initial connection weight,scale factor and translation factor of the WNN,so the initial value of the WNN was optimized.The optimized WNN was used to establish the thermal error prediction model of the lathe spindle,and then it was compared with the un-optimized WNN and BP neural network prediction model.The results show that the BA-WNN has higher prediction accuracy,less residual and stronger prediction ability for the axial thermal error of the spindle.
分 类 号:TH161[机械工程—机械制造及自动化]
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