数控机床运动误差智能补偿方法的研究
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摘要
采用数控加工的目的是提高产品加工的精度和效率,因此加工精度是数控机床最重要的性能指标之一,而对机床实施误差补偿是提高机床加工精度较为有效的方法,但由于数控加工过程具有复杂性、非线性、不确定性等特点,用传统的基于机床成形系统精确数学模型的方法已经难以获得良好的误差补偿效果。本文就是以如何提高数控机床误差补偿精度为目的而展开的。
以国内外研究为基础,应用多体系统误差分析理论建立了数控机床理想运动模型、误差情况下的运动模型、刀具空间姿态理想运动模型以及刀具空间姿态运动误差模型。并以DMG公司生产的五轴数控万能镗铣床DMU 70V(TPPPBRRW)为例给出了成形点空间运动误差模型和刀具姿态运动误差模型。机床误差参数的正确辨识是误差补偿的前提条件之一,本文利用多体系统运动学理论对平动轴和转动轴的几何误差进行了正确辨识。
为了提高误差补偿效果,本文在分析神经网络学习机理的基础上,利用神经网络良好的逼近能力、泛化能力及自学习能力的特点,通过对数控系统进行神经网络辨识,对误差补偿技术和误差控制的神经网络实现方法进行分析,建立了神经网络误差补偿模型。结合了双频激光干涉仪位移测量和直线度测量及三点法回转误差测量法,综合基于多体系统运动学理论建立的误差模型,以及机床几何误差的辨识,利用Malab软件对测量参数进行处理,获得了较为全面的网络训练样本,进一步提高了网络的精度。
通过仿真试验验证了机床成形点空间位置误差模型的正确性和神经网络误差补偿的可行性,对补偿前后的结果分析可以看出,将神经网络技术应用在数控机床误差补偿控制中是可行的,与传统误差补偿方法相比,基于神经网络的数控机床误差补偿具有补偿精度较高、稳定性较好的特点。
The purpose of adopting NC machining is to improve precision and efficiency , so the machining accuracy is one of the most important performance indexes for NC machine. Error compensation is a good way to improve accuracy of NC machining. But the process of NC machining is complicated, non-linear and uncertain. It almost can't be described precisely by mathematical model so that a well result of error compensation using traditional method cann't be got. Just for the purpose of enhancing error compensation accuracy of NC machine tools, this thesis was carried out.
Based on the domestic and foreign research about error compensation, the ideal kinematic model and nonideal kinematic model for NC machine tools, the ideal gesture kinematic model and gesture error kinematic model for cutting tool were established by using the method of movement error analysis of multi-body system (MBS ).The above models were given for five-axes NC boring-milling machine DMU 70V (TPPPBRRW) manufactured by DMG. Correctly identifying of geometric error parameters is one of the most important preconditions for error compensation. The geometric error parameters were identified of translatory axes and rotary axes by using of MBS.
To improve precision of error compensation, a model of error compensation based on Artificial Neural Networks (ANN) theory and identified with ANN through NC system was built with ANN, which possesses excellent approximation ability, generalization ability and self-learning ability. By combining dual-frequency laser interferometer used to measure displacement and straightness, three points method of measure rotary error in which are brought error calculation models based on MBS, identification of machine geometrical error and processing for measure parameters with Matlab software, the comprehensive training samples was gained, which further raised the network precision.
The simulation demonstrates the correctness of volumetric kinematic error models for forming point and the feasibility of the ANN error compensation. Analyzing the result of the error compensation, conclusion that technology of ANN can be applied in controlling of NC machining error compensation can be gotten. Compared with traditional method of error compensation, the method of NC machining error compensation based on ANN possesses characteristics of high precision and good stability.
以国内外研究为基础,应用多体系统误差分析理论建立了数控机床理想运动模型、误差情况下的运动模型、刀具空间姿态理想运动模型以及刀具空间姿态运动误差模型。并以DMG公司生产的五轴数控万能镗铣床DMU 70V(TPPPBRRW)为例给出了成形点空间运动误差模型和刀具姿态运动误差模型。机床误差参数的正确辨识是误差补偿的前提条件之一,本文利用多体系统运动学理论对平动轴和转动轴的几何误差进行了正确辨识。
为了提高误差补偿效果,本文在分析神经网络学习机理的基础上,利用神经网络良好的逼近能力、泛化能力及自学习能力的特点,通过对数控系统进行神经网络辨识,对误差补偿技术和误差控制的神经网络实现方法进行分析,建立了神经网络误差补偿模型。结合了双频激光干涉仪位移测量和直线度测量及三点法回转误差测量法,综合基于多体系统运动学理论建立的误差模型,以及机床几何误差的辨识,利用Malab软件对测量参数进行处理,获得了较为全面的网络训练样本,进一步提高了网络的精度。
通过仿真试验验证了机床成形点空间位置误差模型的正确性和神经网络误差补偿的可行性,对补偿前后的结果分析可以看出,将神经网络技术应用在数控机床误差补偿控制中是可行的,与传统误差补偿方法相比,基于神经网络的数控机床误差补偿具有补偿精度较高、稳定性较好的特点。
The purpose of adopting NC machining is to improve precision and efficiency , so the machining accuracy is one of the most important performance indexes for NC machine. Error compensation is a good way to improve accuracy of NC machining. But the process of NC machining is complicated, non-linear and uncertain. It almost can't be described precisely by mathematical model so that a well result of error compensation using traditional method cann't be got. Just for the purpose of enhancing error compensation accuracy of NC machine tools, this thesis was carried out.
Based on the domestic and foreign research about error compensation, the ideal kinematic model and nonideal kinematic model for NC machine tools, the ideal gesture kinematic model and gesture error kinematic model for cutting tool were established by using the method of movement error analysis of multi-body system (MBS ).The above models were given for five-axes NC boring-milling machine DMU 70V (TPPPBRRW) manufactured by DMG. Correctly identifying of geometric error parameters is one of the most important preconditions for error compensation. The geometric error parameters were identified of translatory axes and rotary axes by using of MBS.
To improve precision of error compensation, a model of error compensation based on Artificial Neural Networks (ANN) theory and identified with ANN through NC system was built with ANN, which possesses excellent approximation ability, generalization ability and self-learning ability. By combining dual-frequency laser interferometer used to measure displacement and straightness, three points method of measure rotary error in which are brought error calculation models based on MBS, identification of machine geometrical error and processing for measure parameters with Matlab software, the comprehensive training samples was gained, which further raised the network precision.
The simulation demonstrates the correctness of volumetric kinematic error models for forming point and the feasibility of the ANN error compensation. Analyzing the result of the error compensation, conclusion that technology of ANN can be applied in controlling of NC machining error compensation can be gotten. Compared with traditional method of error compensation, the method of NC machining error compensation based on ANN possesses characteristics of high precision and good stability.
引文
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[6]李圣怡,戴一帆,彭小强.超精密加工机床及其新技术发展.国防科技大学学报,2000,(2):95-100
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[8]刘又午,章青,王国锋.数控机床误差补偿技术及应用发展动态及展望.制造技术与机床,1998,30(12):5-8
[9]洪迈生,苏恒,熊诗波.数控机床运动误差检测技术.组合机床与自动化加工技术,2002,(1):18-23
[10]张虎,周云飞,唐小琦等.数控机床空间误差的无模测量与补偿.华中科技大学学报,2002,30(1):74-77
[11]刘焕牢,李曦,李斌等.数控机床几何误差和误差补偿关键技术.机械工程师,2003,(1):16-18
[12]Wen-Yuh Jywe,Chien-Hong.Verification and evaluation method for Volumetric and position errors of CNC Machine tools.International Journal of Machine Tools&Manufacture,2000,40(13):1899-1911
[13]刘文彦,周学平,刘辉.现代测试技术.长沙:国防科技大学出版社,1995
[14]周济,周艳红.数控加工技术.北京:国防工业出版社,2001,249-294
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[16]V.G.Badami,S.R.Patterson.A frequency domain method for the measurement of nonlinearity in heterodyne interferometry.Precision Engineering,2000,(10):41-49
[17]汪建新,张国雄.超精密车床激光测量误差补偿系统的研制.天津大学学报.1999,(1):65-68
[18]关信安.双频激光干涉仪.北京:中国计量出版社,1987
[19]王旭,王宏.人工神经网络原理及应用.沈阳:东北大学出版社,2000
[20]Atin T Hagan.神经网络设计.北京:机械工业出版社,2005
[21]犯英学,荆怀靖,陈朔冬,王小平.通过修正NC程序提高加工精度.工具技术,2005,39(40):59-62
[22]Kiridena VSB,Ferreira PM.Kinematic Modeling of Quasistatic Errors of Three-Axis Machining Centers.Int,J.Mach,Tools Manufacture,1994,34(1):85-100
[23]刘丽冰,王广彦,刘又午.复杂机械系统运动误差自动建模技术研究.中国机械工程,2002,11(6):62-68
[24]廖平兰.机床加工过程综合误差实时补偿研究.机械工程学报,1992,28(2):65-74
[25]荆怀靖姚英学,陈朔冬.基于数字化加工的铣削力预测系统的开发.工具技术,2004,38(12):1-9
[26]杜正春,杨建国,关贺,窦小龙.制造机床热误差研究现状与思考.制造业自动化,2003,24(10):1-4
[27]R.Ramesh,M.A.Mannan,A.N.Poo.Error compensation in machine tool-a review Part Ⅰ:geometric,cutting-force induced and fixture-dependent errors.International Journal of Machine Tools&Manufacture,2000:1235-1256
[28]R.Ramesh,M.A.Mannan,A.N.Poo.Error compensation in machine tool-a review PartⅡ:thermal errors.International Journal of MachineTools&Manufacture,2000:1257-1284
[29]Error compensation in CNC turning solely from dimensional measurements of previously machined parts.Annals of the CIRP,1999,48(1):429-432
[30]王清明,卢泽声,梁迎春.亚微米数控车床误差补偿技术研究.中国机械工程,1998,9(12):48-52
[31]Kazuo Yamazaki,Ulrich Mueller,Jiancheng Liu,Jan Braasch.A study on the development of a three dimensional linear system for in-process motion error calibration and compensation of machine tool axes.Annals of the CIRP,2000,49(1):403-406
[32]戴庆辉.先进制造系统.北京:机械工业出版社,2005,263-268
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