[1]常添渊,黄晓华.基于IA-PSO-BP模型的电主轴热误差预测方法[J].机械与电子,2020,(10):52-56.
 CHANG Tianyuan,HUANG Xiaohua.A Thermal Error Prediction Method of Electric Spindle Based on IA-PSO-BP Model[J].Machinery & Electronics,2020,(10):52-56.
点击复制

基于IA-PSO-BP模型的电主轴热误差预测方法()
分享到:

机械与电子[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2020年10期
页码:
52-56
栏目:
自动控制与检测
出版日期:
2020-10-15

文章信息/Info

Title:
A Thermal Error Prediction Method of Electric Spindle Based on IA-PSO-BP Model
文章编号:
1001-2257(2020)10-0052-05
作者:
常添渊黄晓华
南京理工大学机械工程学院,江苏 南京 210094
Author(s):
CHANG TianyuanHUANG Xiaohua
College of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
关键词:
电主轴热误差免疫粒子群BP神经网络
Keywords:
lectric spindle thermal error immune particle swarm BP neural network
分类号:
TH161
文献标志码:
A
摘要:
针对电主轴在运作时因为温升而产生热误差的问题,提出一种基于免疫粒子群优化BP神经网络(IA-PSO-BP)的电主轴热误差预测模型。通过测量电主轴在工作过程中的温升以及热位移,获取建立预测模型所需的数据,使用IA-PSO-BP模型在MATLAB中建立热误差预测模型,并与未经过优化的BP神经网络所建立的模型进行测试对比。结果显示,经过优化的BP神经网络对热误差的补偿能力高达98.4%,和当前工程常用的BP神经网络相比,平均预测误差下降了62.6%,预测误差的均方差下降了66.4%,可见其预测精度得到了显著提升。
Abstract:
A thermal error prediction model of electric spindle, based on BP neural network optimized by immune particle swarm optimization (IA-PSO-BP), was proposed to ddress the thermal error caused by temperature rise in the operation. The data to establish the prediction model was obtained through measuring the temperature rise and thermal displacement of the electric spindle in the working process, and the IA-PSO-BP model was used to establish the thermal error prediction model in MATLAB. Compared with the model established by BP neural network without optimization, the results show that the optimized BP neural network’s thermal error compensation ability is as high as 98.4%, compared with the commonly used BP neural network, the average prediction error is down about 62.6%, mean square error of prediction error has fallen by 66.4%, obviously the prediction accuracy has significantly improved.

参考文献/References:

[1]田良巨.数控加工中心电主轴热误差研究[D]. 天津:天津大学, 2012.
[2]李光龙,陈秀梅.数控机床热误差的控制措施[J].机械工程师,2014(8):3-5.
[3]谢杰,黄筱调,方成刚,等.MEA优化BP神经网络的电主轴热误差分析研究[J].组合机床与自动化加工技术,2017(6):1-4.
[4]马驰, 赵亮, 梅雪松,等.基于粒子群算法与BP网络的机床主轴热误差建模[J].上海交通大学学报, 2016, 50(5):686-695.
[5]代贵松.高速电主轴热特性分析及其热误差建模补偿研究[D].上海:上海交通大学,2014.
[6]ABDULSHAHED A M,LONGSTAFF A P,FLETCHER S.The application of ANFIS prediction models for thermal errorcompensation on CNC machine tools[J].Applied soft computing,2015,27 : 158-168.
[7]何明慧,徐怡,王冉,等.改进的粒子群算法优化神经网络及应用[J].计算机工程与应用,2018,54(19):107-113,128.
[8]何庆.一种基于自适应免疫粒子群算法的多峰函数优化[J].工业控制计算机,2018,31(10):113-115.

相似文献/References:

[1]沈雨苏,陈蔚芳,罗勇,等.电主轴温度场与热变形的仿真与实验研究[J].机械与电子,2018,(12):18.
 SHEN Yusu,CHEN Weifang,LUO Yong,et al.Simulation and Experimental Study on Temperature Field and Thermal Deformation of Electric Spindle[J].Machinery & Electronics,2018,(10):18.
[2]陈建文,胡世军,陈 伟.高速电主轴有限元建模及静动态特性分析[J].机械与电子,2017,(07):27.
 CHEN Jianwen,HU Shijun,CHEN Wei.Finite Element Modeling and Static and Dynamic CharacteristicsAnalysis of High-Speed Electric Spindle[J].Machinery & Electronics,2017,(10):27.
[3]张丽秀,李金鹏,李超群.150MD24Z7.5型电主轴动态误差和热变形实验研究[J].机械与电子,2016,(09):59.
 ZHANG Lixiu,LI Jinpeng,LI Chaoqun.Experiment on Dynamic Errors and Thermal Deformation of 150MD24Z7.5 Motorized Spindle[J].Machinery & Electronics,2016,(10):59.
[4]闫 轩,许 涛,曾柄杰.基于旋滚比的电主轴轴承预紧力优化研究[J].机械与电子,2020,(12):14.
 YAN Xuan,XU Tao,ZEN Bingjie.Optimization Research on Pre-tightening Force of Electric Spindle Bearing Based on Spin--roll Ratio[J].Machinery & Electronics,2020,(10):14.

备注/Memo

备注/Memo:
收稿日期:2020-07-02
作者简介:常添渊(1995-),男,江苏常州人,硕士研究生,研究方向为电主轴综合性能优化;黄晓华(1969-),男,江苏南通人,副教授,研究方向为机电一体化。
更新日期/Last Update: 2020-09-28