数控机床热误差补偿最佳转速选择

为提高数控机床热误差补偿模型在实际工程应用中的补偿精度和稳健性,研究了热误差补偿建模时机床最佳转速状态的选择方法。首先,以Leaderway V-450数控加工中心主轴Z向为研究对象,控制机床主轴在空转状态下,以图谱和恒定转速两种方式进行了多批次实验。然后,采用模糊聚类结合灰色关联度选择温度敏感点并建立多元线性回归模型。最后,分析不同转速类型下模型的预测效果并对同种转速类型下模型预测效果进行相对评价,从而给出热误差补偿建模时机床最佳转速状态的选择方法。实验结果表明,根据国际标准中不同主轴转速类型建立的热误差补偿模型,对于机床热误差预测效果存在较大差异。根据实际工程应用选择的最佳转速状态建立的补偿模型有较好的预测效果。     

数控机床稳健性温度敏感点的选择

为解决温度敏感点变动性带来的模型精度稳健性缺陷,研究了稳健性温度敏感点选择方法.从温度敏感点变动性的机理出发,解释了温度敏感点变动性产生的原因,并在此基础上提出了一种稳健的温度敏感点选择方法,通过全年的实验数据验证了这一方法的有效性.分别使用稳健性温度敏感点选择方法和非稳健性选择方法建立了两个热误差补偿模型,并对它进行...     

基于偏最小二乘的数控机床热误差稳健建模算法

建立预测模型对热误差进行预测和补偿是解决机床热误差问题的常用方法,该方法中模型的预测精度和稳健性易受环境温度影响而明显下降,对此本文提出了基于偏最小二乘法的热误差稳健建模算法。首先使用相关系数法筛选温度敏感点,并建立热误差偏最小二乘回归预测模型。进而基于全年环境温度下的多批次热误差实验数据,分析最佳的温度敏感点个数。最后建立热误差偏最小二乘回归模型,并与普通多元线性回归模型的预测效果比对分析。结果表明本文所提算法平均预测精度为5.7μm,模型稳健性为0.56μm,相较于普通多元线性回归算法,预测精度和稳健性分别提高13.8%和49.5%。说明本文所提的热误差稳健建模算法能够在环境温度变化较大时保持高预测精度和高稳健性。     

数控机床热误差补偿模型稳健性比较分析

数学模型的精度特性和稳健性特性对数控机床热误差补偿技术在实际中的实施性影响不容忽视。对数控加工中心关键点的温度和主轴z向的热变形量采用多种算法建立了预测模型,对不同算法拟合精度进行分析。同时进行全年热误差跟踪试验,获得了机床在不同环境温度和不同主轴转速的试验条件下的敏感点温度和热误差值。以此为基础,对各种预测模型的预测精度进行比较验证不同模型的稳健性。结果表明,多元线性回归算法的最小一乘、最小二乘估计模型以及分布滞后模型在改变试验条件时预测精度下降,而基于支持向量回归机原理的热误差补偿模型仍能保持较好的预测精度,稳健性强。这为数控机床热误差补偿模型的选择提供了具有实用价值的参考,具有很好工程应用性。     

基于CNN-GRU组合神经网络的数控机床进给系统热误差研究

热变形引起的误差是影响数控机床精度的主要因素之一。为了减小热误差对数控机床精度的影响,提出一种基于CNN-GRU组合神经网络的热误差预测方法。通过热误差实验,采集螺旋曲面专用数控机床直线进给系统的温升数据和热误差数据;利用模糊C均值聚类和灰色关联度分析筛选进给系统温度敏感点;以温度敏感点的温升数据和进给系统热误差为数据样本,建立CNN-GRU热误差预测模型。为验证模型的准确性和实用性,与基于CNN-LSTM和基于LSTM的传统热误差预测模型进行预测对比分析,结果表明CNN-GRU模型预测结果的平均绝对误差、均方根误差和决定系数均优于CNN-LSTM模型和LSTM模型,具有较高的预测精度和鲁棒性。提供的热误差模型可为后续误差补偿奠定基础,为数控机床的热误差预测提供思路。     

基于有序聚类的机床热误差测点优化

温度测点的选择直接影响数控机床热误差补偿模型的性能。考虑到温度有序传递的特点,提出了有序聚类测点优化的方法。以试验数据为基础,计算类直径并比较目标误差函数;然后对温度变量分类,确定最佳分类数;通过计算热误差和温度之间的相关系数,确定最优测点。采用定位误差分解建模法结合选取的最优测点建立热误差预测模型,分别与模糊聚类和变量分组测点优化建立的模型进行比较,试验结果表明,有序聚类测点优化法精度较高,具有一定的应用前景。     

基于遗传算法及BP网络的主轴热误差建模

针对基于多输入多输出(MIMO)反向传播(BP)神经网络的热误差建模方法过度依赖于训练样本、通用性与收敛性较差的问题,利用灰色聚类分组与相关分析法对温度变量进行分组并提取热敏感点,利用遗传算法(GA)将预测输出与期望输出的误差绝对值和的倒数作为判断隐含层节点数的准则,对MIMO-BP网络的拓扑结构进行优化,设定输出层残差误差限,实现了网络阈值与权值的有效优化。建立了基于MIMOM-BP与GA-BP的主轴轴向热伸长与径向热倾角的热误差模型。以精密坐标镗床主轴为研究对象,采用五点法对热误差进行测量,验证了测量及建模方法的有效性,表明GA-BP模型可实现不同工况下主轴空间位姿状态的高精度预测,更适合作为热误差补偿模型。     

运用多体理论和神经网络的机床热误差补偿

利用多体系统理论,在典型体的坐标变换中,加入了位移误差矢量和位置误差矢量,形成了具有普遍意义的坐标变换,根据机床拓扑结构的低序体阵列,建立了机床通用误差计算模型。同时,对机床的主轴热变形和床身热变形进行了建模和辨识,通过5个温度敏感点的监测,用常规的5点法对机床主轴热变形进行研究,运用神经网络方法(RBF)建立温度与变形参数模型,将误差参数集成到通用误差模型中。在Makino四轴加工中心进行试验研究,设计出一套多个凸台的空间曲面,比较了不同凸台上的4个点补偿前后空间轮廓数据,误差减少60%,补偿效果显著。     

基于LIBSVM的数控机床热误差建模研究

通过建立数控机床热误差补偿的数学模型是实现机床热误差修正和提高机床精度的有效措施.本文以CL-20A数控车床主轴热变形为实验对象,在大量实验数据的基础上,利用逐步回归分析法找出机床温度敏感点,并采用基于MATLAB平台的支持向量机算法来建立车床主轴热误差数学模型.实验结果表明,所建立的模型能精确把握机床主轴热变形的规律和趋势,对于预测机床主轴热变形,实现实时热补偿具有实用价值.     

数控机床热敏感点识别研究

运用模式识别中的逐步回归法辨识反映机床热态特性的热敏感点,并利用多元回归建立了机床热敏感点处的温升与主轴Ｙ向热位移关系的数学模型。应用表明这是一种在机床温度测量,热误差建模及补偿中选择最佳测温点的有效方法。     

数控机床温度敏感点变动性及其影响

数控机床热误差补偿技术中的核心问题是建立能够反映机床温升与热误差之间的数学模型,其精度和稳健性则取决于模型自变量能否准确地反映机床温度场分布特性,即温度敏感点选择结果是否准确和稳定。通过对Leaderway-V450型数控加工中心主轴Z向的多批次空转数据进行分析发现,温度敏感点存在变动性特征,导致自变量间多重共线性程度发生变化,进而对模型的预测精度和稳健性产生严重影响。由于主成分回归算法具有消除自变量共线性影响作用,故提出采用该算法进行建模,并通过实际机床进行实践检验。结果表明,采用主成分回归算法建模,显著降低了温度敏感点变动性对模型预测精度的影响,能保证模型具有很好的预测精度和稳健性。     

Analysis of CNC machining based on characteristics of thermal errors and optimal design of experimental programs during actual cutting process

The compensation of thermal errors plays a critical role in developing the machine tools of intelligent computer numerically controlled (CNC). According to the international standards, the testing, modeling, and compensation of thermal error of CNC machine tools are carried out only in a so-called idling state where the spindle is free running without any workload. However, in practical applications, machine tools are often applied in the actual cutting state with more influence factors, such as cutting parameters, cooling liquid, and cutting force. Subsequently, the thermal characteristics at idling state and actual cutting state are compared and analyzed in this paper. It was found that the thermal error compensation model under idling state is not precise enough to be applied in actual cutting state. Also, further research finds that different combinations of cutting parameters, such as spindle speed and feed rate, also have influences on the accuracy of prediction and robustness of thermal error model under actual cutting state. Therefore, the cutting parameters of spindle speed, feed rate, depth of cut, and ambient temperature are studied with the usage of the Taguchi method. Through calculating signal-to-noise ratio (SN) of each combination through residual standard deviation of thermal error model, the combination of optimal cutting parameters can be obtained. The resultant analysis shows that the thermal error model under the combination of optimal cutting parameters demonstrates higher accuracy of prediction and better robustness.     

基于Kohonen自组织竞争网络的机床温度测点辨识研究

提出一种基于Kohonen神经网络的温度测点辨识优化算法,用机床进给系统上不同位置处的温度测点变化值及定位误差作为输入样本来训练神经网络。利用该网络的自组织竞争将胜出的结果输出到相应的分类模式中,根据各类分类模式中温度变量与热误差之间的相关系数,确定出机床热关键点。通过多元线性回归理论建立了热误差模型,与基于变量分组优化方法的热误差模型比较发现,该方法具有更好的可行性和有效性。     

Automated measurement and compensation of thermally induced error maps in machine tools

In this paper, a direct method of machine tool calibration is adopted to model and predict thermally induced errors in machine tools. This method uses a laser ball bar (LBB) as the calibration instrument and is implemented on a two-axis computerized numerical control turning center (CNC). Rather than individually measuring the parametric errors to build the error model of the machine, the total positioning errors at the cutting tool and spindle thermal drifts are rapidly measured using the LBB within the same experimental setup. Unlike conventional approaches, the spindle thermal drifts are derived from the true spindle position and orientation measured by the LBB. A neural network is used to build a machine model in an incremental fashion by correlating the measured errors with temperature gradients of the various heat sources during a regular thermal duty cycle. The machine model developed by the neural network is further tested using random thermal duty cycles. The performance of the system is also evaluated through cutting tests under various thermal conditions. A substantial improvement in the overall accuracy was obtained.     

Application of synthetic grey correlation theory on thermal point optimization for machine tool thermal error compensation

This paper presents two new methods to optimize the selection of minimum number of thermal sensors for machine tool thermal error compensation. The two methods, namely, direct criterion method and indirect grouping method, are based on the synthetic grey correlation theory. They are applied to analyze the data of an air cutting experiment on a CNC turning center. After optimization, the number of thermal points reduced from 16 to four. Thus, for machine tool thermal error modeling, the number of temperature variables is greatly reduced while coupling problems among temperature variables can be avoided. A real cutting experiment is then conducted to verify the efficiency of the presented optimization methods under practical manufacturing conditions. The comparison of the results between the model with all 16 thermal sensors and the model with four optimized thermal sensors indicates that, after optimization, the model accuracy is greatly improved.     

双转台五轴机床空间误差补偿技术研究

几何误差、热误差和切削力误差占到了机床总误差的75%,对这3项误差进行控制是提高机床加工精度的关键所在。以双转台五轴机床的空间误差作为研究对象,通过对加工位置、主要热源及电动机电流等相关因素进行分析,确定空间误差建模所需的位移变量、温度变量和切削力变量。以现有的多种误差建模方法为基础,通过对信息融合技术进行研究,提出一种机床空间误差的多模型融合预测方法,建立综合反映几何误差、热误差和切削力误差的最优空间误差模型。最后以DSP为核心,设计空间误差补偿器,实施空间误差补偿,验证补偿效果。结果显示,建立的模型预测精度较高,残差小于2μm,而实施空间误差补偿后,加工零件的轮廓误差也由15μm降到了5μm,补偿效果明显。     

基于灰色关联和模糊聚类的机床温度测点优化

针对机床热误差补偿技术中温度测点优化选择的问题,提出采用基于灰色关联分析和模糊聚类分析相结合的方法对机床温度测点进行优化选择。采用灰色关联分析法计算温度变量与主轴热误差之间的相关系数,并据此优选温度变量,采用模糊聚类分析法对所选择的温度变量进行聚类,确定关键温度变量,结合关键温度变量建立热误差线性回归模型。在精密卧式加工中心MCH63上对该方法进行了试验验证,结果表明,温度测点的数量由29个减少到6个,机床轴向热误差由41.3μm减小到7.6μm。     

精密数控机床主轴热伸长补偿技术研究

针对目前精密数控机床热误差补偿问题,在基于主轴热误差测量系统的基础上,提出一种基于FCM聚类、多元线性回归的热误差补偿模型。通过对某卧式加工中心主轴恒定转速和变速工况下进行温敏点测量,建立关键温敏点与机床主轴热伸长的几何关系,通过补偿结果和切削试验表明该方法可以有效地降低主轴热伸长误差,提升零件的加工精度。     

Thermal Error Modeling Method with the Jamming of Temperature-Sensitive Points’ Volatility on CNC Machine Tools

Aiming at the deficiency of the robustness of thermal error compensation models of CNC machine tools, the mechanism of improving the models’ robustness is studied by regarding the Leaderway-V450 machining center as the object. Through the analysis of actual spindle air cutting experimental data on Leaderway-V450 machine, it is found that the temperature-sensitive points used for modeling is volatility, and this volatility directly leads to large changes on the collinear degree among modeling independent variables. Thus, the forecasting accuracy of multivariate regression model is severely affected, and the forecasting robustness becomes poor too. To overcome this effect, a modeling method of establishing thermal error models by using single temperature variable under the jamming of temperature-sensitive points’ volatility is put forward. According to the actual data of thermal error measured in different seasons, it is proved that the single temperature variable model can reduce the loss of forecasting accuracy resulted from the volatility of temperature-sensitive points, especially for the prediction of cross quarter data, the improvement of forecasting accuracy is about 5 μm or more. The purpose that improving the robustness of the thermal error models is realized, which can provide a reference for selecting the modeling independent variable in the application of thermal error compensation of CNC machine tools.     

An adaptive parameter optimization model and system for sustainable gear dry hobbing in batch production

Gear hobbing technology is one of the most widely used forming processes of gear teeth. And the development of dry hobbing technology provides a solution for realizing productive, economical, and ecological gear production. Since there is no cutting oil for cooling and lubrication in dry hobbing process, the hob tool life, thermal deformation errors of machine tool, and quality of workpiece are sensitive to the cutting parameters, especially the cutting speed and tip chip thickness. Considering this situation, a dry hobbing parameters optimization model with the hobbing efficiency as our objective, and the hobbing cost per piece, gear quality, tact time as constraints was established, in which the cutting speed and tip chip thickness were considered as optimal variables and the material of workpiece, coating of hob, and feed rate were considered comprehensively. An iterative test method is proposed to solve this model. And for the application in automated production line, an online adaptive application system was also developed based on SINUMERIK 840D NC system. The parameters of five different kinds of material gear were optimized by applying this model and system, and the result showed the model and the system were practical.