热弹性效应和数控机床进给系统热动态特性的研究
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摘要
随着数控机床的速度、加速度及加工精度的提高,机床的热态特性对加工精度的影响越来越明显。改善机床的热态特性,已成为机械制造技术中急待解决的问题之一。本学位论文以滚珠丝杠进给系统为对象,以热弹性效应为主线,运用传热学、热弹性力学、控制理论、系统辨识理论等数理手段,以计算机数值模拟以及工程实验分析为研究工具,全面深入地研究了热弹性效应的特征规律、热弹性效应的辨识模型、进给系统的热动态特性、进给系统热动态特性的影响规律以及进给系统热动态特性的建模和预测方法。
交替变化热源的作用会使构件产生热弹性现象。通过传热学理论、GE数值方法及传热实验对一维杆的热弹性效应进行了深入研究,分析了热弹性现象的产生原因和发生规律,提出了热弹性效应具有三个特性:频率保持特性、热变形的实时性和整体性以及相移特性。
由于热弹性效应的理论建模存在困难,本文提出了两种可用于热弹性效应建模的非线性时序神经网络模型(NARX_NN和NARMAX_NN)。NARX_NN和NARMAX_NN模型在非线性系统建模方面的具有结构简单、易于收敛、动态逼近等特性,通过与多层前馈神经网络(MFN)模型、反馈神经网络(RNN)模型及广义最小二乘输出误差(GLS-OE)模型比较,表明了该两种模型能有效地辨识热弹性效应,精确地估算出非线性、动态和时变的热弹性过程。
热弹性效应是热动态特性的一种表现形式。通过单热源作用下滚珠丝杠一维传热的理论分析、GE方法求解及有限元仿真,研究了温度响应和热变形响应之间的“超前性”和“滞后性”,提出了可定义热动态特性的三个特性参数:频率特性、幅值特性和相移特性。通过多变化热源作用下滚珠丝杠的热动态特性的分析,提出了用拉普拉斯变换的方法建立滚珠丝杠热源温度与丝杠上任意位置热变形之间的关系模型,研究滚珠丝杠热变形的稳态响应。
以HUST-FS-001准高速重载进给系统实验台为对象,研究了进给系统在一定进给速度下轴向、径向和回程热误差的变化规律,以及变工况下(进给速度、切削负载和预紧力)的温升-热误差规律,为进给系统热动态特性的辨识和建模奠定基础。研究结果表明:左右轴承座的温升变化与切削负载、预紧力、进给速度等的变化正相关,导轨座、丝杠螺母座的温升与预紧力的变化无关,但能反映负载、进给速度的变化规律,电机座的温升变化随着负载的增大而增大,但与预紧力、进给速度两因素的变化无联系;径向热误差在三种工况变化的实验中变化很小,随机波动,毫无规律;切削负载、预紧力、进给速度与轴向和回程热误差正相关但具有不同的规律性。
由于进给系统结构复杂、热源多样、工况时变,其热动态特性是复杂非线性的。在分析了NARX_NN和NARMAX_NN两种建模方法对多输入多输出(MIMO)系统的建模能力的基础上,用这两种方法建立了HUST-FS-001高速进给系统实验台的热动态特性模型,并与多变量回归模型(MRA)、反馈神经网络(RNN)模型及广义最小二乘输出误差(GLS-OE)模型进行了比较,表明了其具有更好的精度和鲁棒性。
本课题的研究为滚珠丝杠进给系统热误差的分析及动态、实时补偿提供理论依据和指导方法,应用于生产实践具有一定的现实意义和实用价值,也为进一步研究机床整机的热动态特性及演变规律奠定基础。
With the increase of the speed and acceleration, the machining precision is more and more obviously influenced by the thermal characteristic of NC machine tool. To improve the thermal characteristic of NC machine tool has become an urgent problem in the development of manufacturing technology. In this dissertation, according to heat transfer, thermoelasticity dynamics, control theory and system identification, the characterictics and the identifycating modl of thermoelastic effect, the thermal dynamic characteristics and the influencing rules of feed system, the modeling and forecasting methods of the thermal dynamic characteristics of feed system are systematically investigated by combining theoretical analysis with engineering practice.
Thermoelastic phenomenon of object is brought by interchanging heat source. According to theory of heat transfer and Group Explicit numeric method, the thermoelastic effect of unidimensional heat transferring is studied. The being and changing rule of Thermoelastic phenomenon of rod are analysed. Three characteristics of the thermoelastic effect including frequency holding, real-time and integrative thermal deformation and phase shifting, are brought forward.
Because it is difficult to get the theoretic model of thermoelastic effect, two kinds of nonlinear time series neural network models (NARX_NN and NARMAX_NN) are put forward, which are simple in structure, rapid in converging, dynamic fit for modeling of thermoelastic effect. Compared with multi-layer feedforward neural network (MFN), recurrent neural network(RNN) and Generalized least squares-output error (GLS-OE), these two models are proved valid in identifying thermoelastic effect and precise in calculateing nonlinear, dynamic and time varying thermoelastic process.
The thermoelastic effect is one of the expressions of thermal dynamic characteristics. On the basis of theoretic analyse, GE calculating and finite element simulating of unidimensional heat transferring of ballscrew impacted by single heat source, the traits of lead and lag in temperature response and thermal deformation response are studied, and three characteristic parameters including frequency holding, amplitude attenuating and phase shifting are put forward, which can be used for describing thermal dynamic characteristics. By analysis of thermal dynamic characteristics of ballscrew impacted by multiple heat sources, Laplacian arithmetic is brought out to get the relation between thermal deformation on arbitrary location and the temperature of heat sources, and the steady-state thermal deformation response of ballscrew is researched.
Using HUST-FS-001 sub high-speed feed system as an object,changing rules of thermal errors including axial, radial and return thermal errors in certain feed velocity are investigated. Rules of temperature rise and thermal error in changing machining conditions (feed velocity, cutting load and preload) are also investigaged. It is the foundation of identifying and modeling the thermal dynamic characteristics. The results of test show: temperature rises on bearing boxes are positive correlate with feed velocities, cutting loads and preloads; temperature rises on guides housings and screw nut boxes are not correlate with preloads, but they can reflect the changing rules of cutting loads and feed velocities; temperature rises on motor housing are positive correlate with cutting loads, but not correlate with preloads and feed velocity; Under three changing machining conditions ,the changing of radial thermal errors is little, stochastic and ruleless;The axial and return thermal errors are positive correlate with the machining conditions in certain rules.
The thermal dynamic characteristics of feed system are complicated and nonlinear because of the complexity in structure, the multiformity in heat sources and the time varying in machining conditions. On the basis of analyzing the modeling ability of the two nonlinear time series neural network (NARX_NN and NARMAX_NN) on multiple input-multiple output (MIMO) system, the thermal dynamic models of HUST-FS-001 feed system are achieved by these two neural network.Compared with multi-variable regressive analysis (MRA), recurrent neural network (RNN) and Generalized least squares-output error (GLS-OE), these two models are proved more precise, robust and valid in identification of thermoelastic effect.
The achievements of this dissertation provide significant theoretic foundation of real-time compensating for the thermal error of feed system. It is practical and valuable to apply these achievements to production practise. Also, it is the foundation of further research in thermal dynamic characteristics and evolving rules of integrated machine tools.
交替变化热源的作用会使构件产生热弹性现象。通过传热学理论、GE数值方法及传热实验对一维杆的热弹性效应进行了深入研究,分析了热弹性现象的产生原因和发生规律,提出了热弹性效应具有三个特性:频率保持特性、热变形的实时性和整体性以及相移特性。
由于热弹性效应的理论建模存在困难,本文提出了两种可用于热弹性效应建模的非线性时序神经网络模型(NARX_NN和NARMAX_NN)。NARX_NN和NARMAX_NN模型在非线性系统建模方面的具有结构简单、易于收敛、动态逼近等特性,通过与多层前馈神经网络(MFN)模型、反馈神经网络(RNN)模型及广义最小二乘输出误差(GLS-OE)模型比较,表明了该两种模型能有效地辨识热弹性效应,精确地估算出非线性、动态和时变的热弹性过程。
热弹性效应是热动态特性的一种表现形式。通过单热源作用下滚珠丝杠一维传热的理论分析、GE方法求解及有限元仿真,研究了温度响应和热变形响应之间的“超前性”和“滞后性”,提出了可定义热动态特性的三个特性参数:频率特性、幅值特性和相移特性。通过多变化热源作用下滚珠丝杠的热动态特性的分析,提出了用拉普拉斯变换的方法建立滚珠丝杠热源温度与丝杠上任意位置热变形之间的关系模型,研究滚珠丝杠热变形的稳态响应。
以HUST-FS-001准高速重载进给系统实验台为对象,研究了进给系统在一定进给速度下轴向、径向和回程热误差的变化规律,以及变工况下(进给速度、切削负载和预紧力)的温升-热误差规律,为进给系统热动态特性的辨识和建模奠定基础。研究结果表明:左右轴承座的温升变化与切削负载、预紧力、进给速度等的变化正相关,导轨座、丝杠螺母座的温升与预紧力的变化无关,但能反映负载、进给速度的变化规律,电机座的温升变化随着负载的增大而增大,但与预紧力、进给速度两因素的变化无联系;径向热误差在三种工况变化的实验中变化很小,随机波动,毫无规律;切削负载、预紧力、进给速度与轴向和回程热误差正相关但具有不同的规律性。
由于进给系统结构复杂、热源多样、工况时变,其热动态特性是复杂非线性的。在分析了NARX_NN和NARMAX_NN两种建模方法对多输入多输出(MIMO)系统的建模能力的基础上,用这两种方法建立了HUST-FS-001高速进给系统实验台的热动态特性模型,并与多变量回归模型(MRA)、反馈神经网络(RNN)模型及广义最小二乘输出误差(GLS-OE)模型进行了比较,表明了其具有更好的精度和鲁棒性。
本课题的研究为滚珠丝杠进给系统热误差的分析及动态、实时补偿提供理论依据和指导方法,应用于生产实践具有一定的现实意义和实用价值,也为进一步研究机床整机的热动态特性及演变规律奠定基础。
With the increase of the speed and acceleration, the machining precision is more and more obviously influenced by the thermal characteristic of NC machine tool. To improve the thermal characteristic of NC machine tool has become an urgent problem in the development of manufacturing technology. In this dissertation, according to heat transfer, thermoelasticity dynamics, control theory and system identification, the characterictics and the identifycating modl of thermoelastic effect, the thermal dynamic characteristics and the influencing rules of feed system, the modeling and forecasting methods of the thermal dynamic characteristics of feed system are systematically investigated by combining theoretical analysis with engineering practice.
Thermoelastic phenomenon of object is brought by interchanging heat source. According to theory of heat transfer and Group Explicit numeric method, the thermoelastic effect of unidimensional heat transferring is studied. The being and changing rule of Thermoelastic phenomenon of rod are analysed. Three characteristics of the thermoelastic effect including frequency holding, real-time and integrative thermal deformation and phase shifting, are brought forward.
Because it is difficult to get the theoretic model of thermoelastic effect, two kinds of nonlinear time series neural network models (NARX_NN and NARMAX_NN) are put forward, which are simple in structure, rapid in converging, dynamic fit for modeling of thermoelastic effect. Compared with multi-layer feedforward neural network (MFN), recurrent neural network(RNN) and Generalized least squares-output error (GLS-OE), these two models are proved valid in identifying thermoelastic effect and precise in calculateing nonlinear, dynamic and time varying thermoelastic process.
The thermoelastic effect is one of the expressions of thermal dynamic characteristics. On the basis of theoretic analyse, GE calculating and finite element simulating of unidimensional heat transferring of ballscrew impacted by single heat source, the traits of lead and lag in temperature response and thermal deformation response are studied, and three characteristic parameters including frequency holding, amplitude attenuating and phase shifting are put forward, which can be used for describing thermal dynamic characteristics. By analysis of thermal dynamic characteristics of ballscrew impacted by multiple heat sources, Laplacian arithmetic is brought out to get the relation between thermal deformation on arbitrary location and the temperature of heat sources, and the steady-state thermal deformation response of ballscrew is researched.
Using HUST-FS-001 sub high-speed feed system as an object,changing rules of thermal errors including axial, radial and return thermal errors in certain feed velocity are investigated. Rules of temperature rise and thermal error in changing machining conditions (feed velocity, cutting load and preload) are also investigaged. It is the foundation of identifying and modeling the thermal dynamic characteristics. The results of test show: temperature rises on bearing boxes are positive correlate with feed velocities, cutting loads and preloads; temperature rises on guides housings and screw nut boxes are not correlate with preloads, but they can reflect the changing rules of cutting loads and feed velocities; temperature rises on motor housing are positive correlate with cutting loads, but not correlate with preloads and feed velocity; Under three changing machining conditions ,the changing of radial thermal errors is little, stochastic and ruleless;The axial and return thermal errors are positive correlate with the machining conditions in certain rules.
The thermal dynamic characteristics of feed system are complicated and nonlinear because of the complexity in structure, the multiformity in heat sources and the time varying in machining conditions. On the basis of analyzing the modeling ability of the two nonlinear time series neural network (NARX_NN and NARMAX_NN) on multiple input-multiple output (MIMO) system, the thermal dynamic models of HUST-FS-001 feed system are achieved by these two neural network.Compared with multi-variable regressive analysis (MRA), recurrent neural network (RNN) and Generalized least squares-output error (GLS-OE), these two models are proved more precise, robust and valid in identification of thermoelastic effect.
The achievements of this dissertation provide significant theoretic foundation of real-time compensating for the thermal error of feed system. It is practical and valuable to apply these achievements to production practise. Also, it is the foundation of further research in thermal dynamic characteristics and evolving rules of integrated machine tools.
引文
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