基于信息融合的五轴机床热误差建模技术

五轴机床能对复杂的自由曲面进行加工。对五轴机床热误差进行控制,是提高其加工精度的关键所在。针对现有热误差建模方法预测精度较低、通用性和鲁棒性较差的问题,提出一种基于信息融合的五轴机床热误差建模方法。与传统建模方法相比,通过实时调整模型参数,该融合预测方法能够用于不同类型、不同操作条件的机床。将该方法应用于一台双转台五轴机床的实验研究,建立了该机床热误差的融合预测模型。实验结果表明,该方法能够提高热误差模型的预测精度及鲁棒性,从而提高五轴机床加工精度。     

基于混合粒子群算法优化BP神经网络的机床热误差建模

为了降低机床热误差对主轴加工精度的影响,采用了混合粒子群算法优化BP神经网络结构,并对优化结果进行实验验证.引用了粒子群算法耦合遗传算法,给出BP神经网络结构简图,通过混合粒子群算法优化BP神经网络结构.构造机床热误差优化目标函数,采用混合粒子群算法优化目标函数,给出了混合粒子群算法优化BP神经网络流程图.建立BP神经网络热误差预测模型和BP神经网络热误差优化模型,采用三轴立式铣床对两种预测结果进行实验验证.实验结果表明:采用BP神经网络热误差预测模型,机床y轴、z轴预测结果与实验结果偏差最大值分别为6.9μm和6.7μm;采用BP神经网络热误差优化模型,机床y轴、z轴预测结果与实验结果偏差最大值分别为3.3μm和3.5μm.采用混合粒子群算法优化BP神经网络结构,能够提高机床热误差预测精度.     

数控机床进给轴热误差补偿技术研究综述

机床在内外热源共同作用下产生热变形,严重影响机床的精度稳定性与零件加工精度,如何抑制机床热误差是一个重要的研究领域.介绍了机床热误差避免方法和热误差补偿方法的研究进展.分析了直线进给轴误差的成因,并阐述了有/无预紧条件下丝杠热变形过程及机理.介绍了温度测点位置优化方法,以及数据驱动与机理驱动的热误差建模理念、方法及特点.介绍了旋转进给轴热误差测试方法,并给出了其建模及补偿效果.最后,对进给轴热误差补偿技术研究进行了展望.     

数控机床热误差建模及补偿研究

数控机床的热误差是影响机床精度的关键因素,因此需要对其进行预测和补偿。首先通过多工况下的定位误差测量和进给轴测量点的温度监测,分析了进给轴温度场和热误差的分布变化特征;利用多元线性回归表征温度变化与进给轴膨胀率的定量关系从而完成热误差的预测建模,针对三轴数控立式铣床进行了热误差补偿。实验结果显示:基于修正的最小二乘法的热误差参考点预测模型更符合实际工况,与补偿前相比机床进给轴的精度提升了47.5%。     

数控机床热误差建模与补偿

数控机床热变形误差对零件加工精度有重大影响。基于GA-SVR(遗传算法-支持向量回归机)的数控机床热误差建模方法要点有三:其一是数据采样,用不同传感器测量机床关键点的温度与机床主轴变形量。其二是数据训练,把获得的数据进行支持向量回归机建模训练,同时使用遗传算法寻找支持向量回归机相关参数的最优值。其三是数据建模,建立机床热误差模型,并验证模型的准确度。仿真及实验结果表明,基于GA-SVR的数控机床热误差建模方法具有精度高和鲁棒性强的特点。并依此算法建立了以DSP和A/D为核心的热误差补差补偿器。     

基于分段拟合的机床大尺寸工作台热误差补偿模型

大型机床工作台在往复运动过程中,丝杠螺母会产生大量的热,一部分热量从螺母传入工作台导致工作台两侧翘曲,使工作台不同位置产生不同热误差。为提高大尺寸机床工作台的纵向热误差补偿精度,提出分段拟合热误差建模预测方法。该方法是沿工作台横向在多个位置建立对应点的纵向热误差模型,然后由各点单模型预测值进行分段拟合建立工作台整体预测模型,利用分段拟合模型实现对工作台任意位置热误差预测。同时为了提高热误差模型预测精度和鲁棒性,采用粒子群优化算法根据实时反馈热误差数据对模型参数辨识,使热误差模型能适应机床最新的工作状态。在一台三坐标铣床工作台上进行试验,建立X轴快速运动时工作台纵向热误差模型,试验结果表明:该方法鲁棒性好预测精度高,能够实现大尺寸工作台任意位置的热误差补偿,且具有一定的通用性。     

基于互信息法和改进模糊聚类的机床热关键点优化研究

基于热误差模型进行机床热误差补偿是保证数控机床加工精度的一种有效方法,温度测点的布置和辨识会直接影响热误差建模的精确性和鲁棒性。本文提出一种互信息和改进模糊聚类法相结合的机床热关键点优化方法。以机床不同位置处的多个测点温度值及工件热变形作为分析数据,通过计算温度变量与热变形之间的平均互信息量,获得其综合关联度矩阵,确定二者之间的相关性后初选温度变量。根据改进模糊聚类法、F统计量和复判定系数对初选的温测点进行聚类,并结合温度变量与热变形之间的综合关联度值提取机床热关键点,从而实现测点优化。将基于该方法所得到的热误差模型与采用变量分组优化法获得的热误差模型进行比较,结果显示采用该方法进行热误差建模,机床X轴和Y轴的热变形预测精度得到显著提高,有利于改善加工精度。     

基于互信息法和改进模糊聚类的温度测点优化

基于热误差模型进行机床热误差补偿是保证数控机床加工精度的一种有效方法,温度测点的布置和辨识会直接影响热误差建模的精确性和鲁棒性。本文提出一种互信息和改进模糊聚类法相结合的机床热关键点优化方法。以机床不同位置处的多个测点温度值及工件热变形作为分析数据,通过计算温度变量与热变形之间的平均互信息量,获得其综合关联度矩阵,确定二者之间的相关性后初选温度变量。根据改进模糊聚类法、F统计量和复判定系数对初选的温测点进行聚类,并结合温度变量与热变形之间的综合关联度值提取机床热关键点,从而实现测点优化。将基于该方法所得到的热误差模型与采用变量分组优化法获得的热误差模型进行比较,结果显示采用该方法进行热误差建模,机床X轴和Y轴的热变形预测精度得到显著提高,有利于改善加工精度。     

数控立式加工中心主轴热变形测试与建模研究

热误差是影响数控立式加工中心制造精度的主要因素,机床主轴热变形是机床热误差的主要来源。针对传统机床主轴热变形单因素建模的不足,测试了某立式加工中心在几种转速工况下的温升及Z轴热变形,对测试数据进行了分析与处理,作出了在几种工况下主轴热变形与时间的关系曲线。对主轴热变形做了二元线性回归分析建模,以及人工神经网络建模,通过对比这两种方法建模的误差,表明神经网络建模优于二元回归建模。     

数控机床综合误差分析与建模研究

分析了影响机床精度的误差来源及运动副的误差运动学原理。以一台三轴数控机床为研究对象,利用低序体阵列描述多体系统拓扑结构,用特征矩阵表示多体系统中间体的相对位置和姿态,建立误差综合数学模型,模型中不仅包含了几何误差且包含了热误差和切削力误差,可为其他类型的机床误差综合建模及补偿提供参考。     

基于IA-PSO-BP模型的电主轴热误差预测方法

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。     

Thermal error modeling by integrating GA and BP algorithms for the high-speed spindle

Thermal error is one of primary reasons affecting the cutting accuracy of a machine tool. Especially, high-speed spindle’s thermal error restricts the improvement of high-precision machine tool accuracy. In this paper, the traditional back propagation (BP) neural network modeling principle is firstly analyzed. Then, considering the BP network, which is simple and adaptive but converges slowly and is easy to reach local minima, a genetic algorithm (GA) is introduced to optimize BP network’s initial weights and thresholds. Lastly, the network is trained using combined GA and BP technologies with practical thermal error sample data. In experiments of thermal error prediction of the high-speed spindle in a machine tool, the average compensation rate is increased from 89.03 % of BP model to 93.155 % of GABP model. Therefore, GABP model shows its effectiveness in quickly solving the global minimum searching problem.     

基于贝叶斯神经网络的机床热误差建模

热误差严重影响着机床的加工精度,对机床关键部件进行热特性分析是开发精密机床的重要环节。通过测量包括数控机床的特殊位置温度和定位误差在内的热特性,研究了温升与定位误差之间的关系,提出了一种基于贝叶斯神经网络的热误差建模方法。通过K-means聚类和相关系数法来选择温度敏感点,可以有效地抑制温度测量点之间的多重共线性问题。结果表明:通过使用贝叶斯神经网络能提高机床88.0159%的精度,比BP神经网络高出15.7638%,与BP神经网络模型相比,贝叶斯神经网络具有更加优良预测性能。贝叶斯神经网络模型为降低机床热误差的影响提供了新思路。     

Theory analysis and system identification methods on thermal dynamics characteristics of ballscrews

Empirical model of machine tools on thermal error has been widely researched, which can compensate for thermal error to some extent but not suitable for thermal dynamic errors produced by dynamic heat sources. The thermoelastic phenomenon of unidimensional heat transfer of ballscrews influenced by changeable heat sources is analyzed based on the theory of heat transfer. Two methods for system identification (the least square system identification and BP artificial neural network (ANN) system identification) are put forward to establish a dynamic characteristic model of thermal deformation of ballscrews. The model of thermal error of the X axis in a feed system of DM4600 vertical miller is established with a fine identification effect. Comparing the results of the two identification methods, the BP ANN system identification is more precise than the least square system identification.     

Theory analysis and system identification methods on thermal dynamics characteristics of ballscrews

Empirical model of machine tools on thermal error has been widely researched, which can compensate for thermal error to some extent but not suitable for thermal dynamic errors produced by dynamic heat sources. The thermoelastic phenomenon of unidimensional heat transfer of ballscrews influenced by changeable heat sources is analyzed based on the theory of heat transfer. Two methods for system identification (the least square system identification and BP artificial neural network (ANN) system identification) are put forward to establish a dynamic characteristic model of thermal deformation of ballscrews. The model of thermal error of the X axis in a feed system of DM4600 vertical miller is established with a fine identification effect. Comparing the results of the two identification methods, the BP ANN system identification is more precise than the least square system identification.     

基于灰色系统理论的GM2000A数控机床热误差的智能补偿方式及应用

众所周知对于超精密机床而言,热误差是影响数控机床加工精度的重要因素之一,可达机床加工精度总误差的70%。因此减少热误差对数控机床的影响至关重要。要提高加工精度,减少热误差,就必须对其进行有效的补偿。以GM2000A龙门加工中心为对象介绍了热误差的测量方法,采用灰色系统理论中的灰色关联分析法建立测温点和热误差之间的相关程度达到优化测温点的目的,使输入数据更趋于合理。最后用BP神经网络建立温升和机床热误差之间的数学模型,MATLAB仿真实验结果表明了补偿效果的可行性。     

The thermal error optimization models for CNC machine tools

Thermal error compensation is becoming a cost-effective way to improve accuracy of machine tools especially with the increasing demand for machining accuracy in recent years. The compensation effectiveness mainly depends on the accuracy of the thermal error model. In order to explore the robustness, versatility, and prediction accuracy of the thermal error modeling, linear regression (LR) model, backpropagation (BP) network model, and radial basis function (RBF) network model are developed, analyzed, and compared. Experimental validation on a high precision four-axes machining center demonstrates that LR model has high prediction accuracy yet it has low robustness because the estimation of the regression coefficients and thermal key points are strongly correlated to the measurement data and their noise level. The neural network model is more adaptive to the case of different feed rates, rotational speeds, and ambient temperatures and has certain versatility on machine tools of the same type. The fitting accuracy and the prediction correctness of the BP network usually vary according to the hidden neurons, thresholds, and weights and cannot achieve the peak performance simultaneously. Experimental results show that the thermal error in Z axial direction could be reduced to as less as 25% of the original thermal error with compensation using the RBF model under the machining conditions of various feed rates and rotational speeds. It is also demonstrated that RBF model could improve the thermal precision in Z axle of the machine tool by about 65% under the distinct environmental temperature conditions.     

关节式坐标测量机热变形误差及修正

关节式坐标测量机工作温度范围宽,可以在线测量,应用领域越来越宽.这也使得其热变形误差随着对测量机测量精度要求的提高越来越受到关注,并且对其必须加以修正.简要分析关节式坐标测量机主要部件由于温度变化对测量的影响.关节式坐标测量机热变形误差模型用单隐层带反向传播前馈神经网络建立,网络的输入是测量机两个温度参数和测头的坐标值,输出为测头坐标相对于20℃该点的变化量.通过试验获得数据样本,训练所建模型并进行仿真.试验结果表明所建模型对关节式坐标测量机热变形误差修正是有效的.     

数控慢走丝线切割机床热变形补偿方法的研究

由实验测得专家经验测点的温度与热形变误差量,通过模糊聚类的分析方法找出影响误差精度的主要温度测点,分别利用多元线性回归的分析方法和遗传算法优化BP神经网络法建立热变形误差的数学模型,将两种方式进行对比分析哪种方式更优.     

Identification of thermal error in a feed system based on multi-class LS-SVM

Research of thermal characteristics has been a key issue in the development of high-speed feed system. The thermal positioning error of a ball-screw is one of the most important objects to consider for high-accuracy and high-speed machine tools. The research work undertaken herein ultimately aims at the development of a comprehensive thermal error identification model with high accuracy and robust. Using multi-class least squares support vector machines (LS-SVM), the thermal positioning error of the feed system is identified with the variance and mean square value of the temperatures of supporting bearings and screw-nut as feature vector. A series of experiments were carried out on a self-made quasi high-speed feed system experimental bench HUST-FS-001 to verify the identification capacity of the presented method. The results show that the recommended model can be used to predict the thermal error of a feed system with good accuracy, which is better than the ordinary BP and RBF neural network. The work described in this paper lays a solid foundation of thermal error prediction and compensation in a feed system.