基于SSO算法优化神经网络的数控机床热误差建模

doi:10.12068/j.issn.1005-3026.2021.11.008

东北大学学报（自然科学版） ›› 2021, Vol. 42 ›› Issue (11): 1569-1578.DOI: 10.12068/j.issn.1005-3026.2021.11.008

基于SSO算法优化神经网络的数控机床热误差建模

黄智¹，刘永超¹，廖荣杰²，曹旭军²

(1. 电子科技大学机械与电气工程学院，四川成都611731; 2. 四川成飞集成科技股份有限公司，四川成都610091)

修回日期:2020-08-28 接受日期:2020-08-28 发布日期:2021-11-19
通讯作者: 黄智
作者简介:黄智(1977-)，男，四川成都人，电子科技大学副教授.
基金资助:
国家科技重大专项(2017ZX04002001)；四川省科技计划项目(2020JDRC0173).

Thermal Error Modeling of Numerical Control Machine Tools Based on Neural Network Neural Network by Optimized SSO Algorithm

HUANG Zhi¹， LIU Yong-chao¹， LIAO Rong-jie²， CAO Xu-jun²

1. School of Mechanical and Electrical Engineering， University of Electronic Science and Technology， Chengdu 611731， China; 2. Sichuan Chengfei Integration Technology Corporation， Chengdu 610091， China.

Revised:2020-08-28 Accepted:2020-08-28 Published:2021-11-19
Contact: LIU Yong-chao
About author:-
Supported by:
-

摘要/Abstract

摘要： 针对影响五轴数控机床加工精度的复杂热特性，提出了一种用于摇篮式五轴数控机床热误差建模方法.该方法主要采用鲨鱼嗅觉优化(SSO)算法和神经网络的复合建模方式，有效提高了机床热误差预测模型的精度和建模效率.首先通过使用热成像仪筛选出机床的温度敏感点，然后将温度传感器布置在机床热敏感点的位置，将采集到的热特性数据采用本文所提方法进行热误差建模，结果表明，该方法在建模速度和精度上要优于ABC和PSO神经网络，最后将该热误差预测模型应用于五轴数控机床热误差补偿实验，将试件加工精度提高了32%.

关键词: 五轴数控机床;鲨鱼嗅觉优化算法;热误差建模;热误差补偿;温度关键点

Abstract: In order to explore the complex thermal characteristics of five-axis NC(numerical control) machine tools， a method for thermal error modeling of cradle five-axis NC machine tools was proposed. The principle of shark smell optimization(SSO)algorithm and neural network composite modeling was adopted， which effectively improved the accuracy and modeling efficiency of the machine tool thermal error prediction model. Firstly， the temperature sensitive point was screened by using the thermal imager， and then the temperature sensor was placed at the position of the heat sensitive point of the machine tool. The collected thermal characteristic data were modeled by the above method. The results showed that the method is better than ABC neural network and PSO neural network in terms of modeling speed and accuracy. Finally， the model was applied to the thermal error compensation experiment of the five-axis machine tool， which improves its accuracy by 32%.

Key words: five-axis NC(numerical control) machine tool; shark smell optimization(SSO)algorithm; thermal error modeling; thermal error compensation; temperature key point

中图分类号:

TH117

黄智，刘永超，廖荣杰，曹旭军. 基于SSO算法优化神经网络的数控机床热误差建模[J]. 东北大学学报（自然科学版）, 2021, 42(11): 1569-1578.

HUANG Zhi， LIU Yong-chao， LIAO Rong-jie， CAO Xu-jun. Thermal Error Modeling of Numerical Control Machine Tools Based on Neural Network Neural Network by Optimized SSO Algorithm[J]. Journal of Northeastern University(Natural Science), 2021, 42(11): 1569-1578.

参考文献

[1]Liu Y S，Miao E M，Liu H，et al.Robust machine tool thermal error compensation modelling based on temperature-sensitive interval segmentation modelling technology［J］.International Journal of Advanced Manufacturing Technology，2020，106(2):655-669.
[2]黄智，贾臻杰，邓涛，等.基于支持向量机的静压转台热误差补偿［J］.浙江大学学报(工学版)，2019，8(8):1594-1601.(Huang Zhi，Jia Zhen-jie，Deng Tao，et al.Thermal error compensation of static pressure turntable based on support vector machine［J］.Journal of Zhejiang University(Engineering Science)，2019，8(8):1594-1601.)
[3]Sun Y H，Xiao W，Hu R K，et al.Thermal characteristics analysis of mill head of five-axis CNC mill machine based on finite element method［J］.Applied Mechanics and Materials，2013，446/447:509-512.
[4]李朕均，赵春雨，闻邦椿，等.数控机床进给系统热源发热率辨识与热误差预测［J］.东北大学学报(自然科学版)，2019，40(9):1305-1309.(Li Zhen-jun，Zhao Chun-yu，Wen Bang-chun，et al.Heat source rate identification and thermal error predictions of ball screw feed drive system for CNC machine tools［J］.Journal of Northeastern University(Natural Science)，2019，40(9):1305-1309.)
[5]Li T J，Liu K.Dynamic model on thermally induced characteristics of ball screw systems［J］. International Journal of Advanced Manufacturing Technology，2019，103(1):3703-3715.
[6]Li Y，Zhao W H，Lan S H，et al.A review on spindle thermal error compensation in machine tools［J］.International Journal of Machine Tools and Manufacture，2015，95(4):20-38.
[7]Miao E M，Gong Y Y，Dang L C，et al.Temperature-sensitive point selection of thermal error model of CNC machining center［J］.The International Journal of Advanced Manufacturing Technology，2014，74(5/6/7/8):681-691.
[8]Guo Q，Fan S，Xu R F，et al.Spindle thermal error optimization modeling of a five-axis machine tool［J］.Chinese Journal of Mechanical Engineering，2017，30(3):746-753.
[9]Abdulshahed A M，Longstaff A P，Fletcher S，et al.Thermal error modelling of a gantry-type 5-axis machine tool using a grey neural network model［J］.Journal of Manufacturing Systems，2016，41:130-142.
[10]Blaser P，Florentina P，Mori K，et al.Adaptive learning control for thermal error compensation of 5-axis machine tools［J］.Journal of Manufacturing Systems，2017，44:231-238.
[11]Gebhardt M，Ess M，Weikert S，et al.Phenomenological compensation of thermally caused position and orientation errors of rotary axes［J］.Journal of Manufacturing Processes，2013，15(4):452-459.
[12]Gebhardt M，Mayr J，Furrer N，et al.High precision grey-box model for compensation of thermal errors on five-axis machines［J］.CIRP Annals—Manufacturing Technology，2014，63(1):509-512.
[13]Du Z C，Yao S Y，Yang J G.Thermal behavior analysis and thermal error compensation for motorized spindle of machine tools［J］.International Journal of Precision Engineering and Manufacturing，2015，16(7):1571-1581.
[14]Mare M，Horejs O.Modelling of cutting process impact on machine tool thermal behaviour based on experimental data［J］.Procedia CIRP，2017，58(10):152-157.
[15]Horejs O，Mare M，Hornych J.A general approach to thermal error modelling of machine tools［J］.Machines Usinageà Grande Vitesse(MUGV)，Clermont Ferrand，2014，10(4):1-10.
[16]Abedinia O，Amjady N.Short-term wind power prediction based on hybrid neural network and chaotic shark smell optimization［J］.International Journal of Precision Engineering and Manufacturing—Green Technology，2015，2(3):245-254.
[17]Li G C，Zhou H G，Jing X W，et al.An intelligent wheel position searching algorithm for cutting tool grooves with diverse machining precision requirements［J］.International Journal of Machine Tools and Manufacture，2017，122(11):149-160.
[18]Bozorg-haddad O.Advanced optimization by nature-inspired algorithms［J］.Studies in Computational Intelligence，2018，720(8):93-105.
[19]Association J S.Test conditions for machining centres—part 7:accuracy of finished test pieces:ISO 10791-7 ［S］.London:BSI Standards Limited，2014.

基于SSO算法优化神经网络的数控机床热误差建模

Thermal Error Modeling of Numerical Control Machine Tools Based on Neural Network Neural Network by Optimized SSO Algorithm

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