Thermal error modelling for a high-precision feed system in varying conditions based on an improved Elman network

被引：14

作者：

Yang, Haojin ^{[1
,2
]}

Xing, Renpeng ^{[3
]}

Du, Fuxin ^{[1
,2
]}

机构：

[1] Shandong Univ, Sch Mech Engn, Jinan 250061, Shandong, Peoples R China

[2] Shandong Univ, Minist Educ, Key Lab High Efficiency & Clean Mech Manufacture, Jinan 250061, Shandong, Peoples R China

[3] Shandong Acad Sci, Inst Automat, Jinan 250014, Shandong, Peoples R China

来源：

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY | 2020年 / 106卷 / 1-2期

基金：

中国国家自然科学基金;

关键词：

Thermal error modelling; Elman neural network; Differential evolution algorithm; Feed system of machine tool; NEURAL-NETWORK; IDENTIFICATION; COMPENSATION;

D O I：

10.1007/s00170-019-04605-1

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A two-axis differential micro-feed system (TDMS) can overcome the accuracy limitation of the conventional drive feed system (CDFS). However, the heat induced by friction in the ball screw, bearings, and motors will lead to axial thermal deformation of the screw, with the deformation being the primary factor restricting the high precision micro-feed. Elman neural networks (ENs) are employed to carry out the thermal error modelling in this paper. To improve the performance of ENs, the differential evolution (DE) algorithm is used to optimize the initial weights and thresholds of the ENs. Complex operating conditions of the TDMS are also considered in the model. The experimental results show that the thermal error residual decreased from 1.73 to 0.88 mu m for the DE-ELMAN model. Moreover, the proposed method of thermal error modelling proved to be accurate and robust when used in the varying conditions.

引用

页码：279 / 288

页数：10

共 20 条

[1] Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera
Abdulshahed, Ali M.
Longstaff, Andrew P.
Fletcher, Simon
Myers, Alan
[J]. APPLIED MATHEMATICAL MODELLING, 2015, 39 (07) : 1837 - 1852
[2] [Anonymous], 1990, CIRP Ann
[3] Chen Cheng, 2014, Optics and Precision Engineering, V22, P704, DOI 10.3788/OPE.20142203.0704
[4] Robust modelling and prediction of thermally induced positional error based on grey rough set theory and neural networks
Cheng, Qiang
Qi, Zhuo
Zhang, Guojun
Zhao, Yongsheng
Sun, Bingwei
Gu, Peihua
[J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2016, 83 (5-8) : 753 - 764
[5] Chern SJ, 2006, I S INTELL SIG PROC, P850
[6] Position error compensation of semi-closed loop servo system using support vector regression and fuzzy PID control
Deng, C.
Xie, S. Q.
Wu, J.
Shao, X. Y.
[J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2014, 71 (5-8) : 887 - 898
[7] Du Fuxin, 2018, Journal of Mechanical Engineering, V54, P195, DOI 10.3901/JME.2018.09.195
[8] Cross-coupled intelligent control for a novel two-axis differential micro-feed system
Du, Fuxin
Feng, Xianying
Li, Peigang
Wang, Jiawei
Wang, Zhaoguo
Yu, Chen
[J]. ADVANCES IN MECHANICAL ENGINEERING, 2018, 10 (06):
[9] Identification and compensation of friction for a novel two-axis differential micro-feed system
Du, Fuxin
Zhang, Mingyang
Wang, Zhaoguo
Yu, Chen
Feng, Xianying
Li, Peigang
[J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2018, 106 : 453 - 465
[10] Modeling, identification and analysis of a novel two-axis differential micro-feed system
Du, Fuxin
Li, Peigang
Wang, Zhaoguo
Yue, Mingjun
Feng, Xianying
[J]. PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY, 2017, 50 : 320 - 327

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