精密卧式加工中心主轴轴向热误差补偿方法

热误差是精密机床最主要的误差源之一。主轴是机床的关键部件,其热误差直接影响机床的加工精度。文章以某型号精密卧式加工中心主轴为对象,对其温度场和热变形进行了仿真分析。根据仿真结果发现主轴轴向热变形更严重,并结合机床结构确定温度传感器布置位置。在此基础上,对不同转速下主轴部分位置温度和轴向热误差进行现场测试。运用最小二乘法建立热误差补偿模型,直接结合机床FANUC数控系统实施主轴轴向热误差补偿。经实验验证,补偿后主轴轴向热误差减小了85%以上。     

数控机床热误差补偿建模综述

热误差建模技术是决定热误差补偿能否有效进行的关键,对提高数控机床的加工精度至关重要。介绍数控机床热误差建模的国内外研究状况,阐述国内外常用的几种主要的热误差建模方法,即人工智能法、统计分析法、灰色系统法等,探讨各种方法的特点,指出目前研究存在的问题,并展望未来的发展。     

数控车床热误差实时补偿研究

热误差是数控加工中的主要误差源之一,对零件加工精度有非常大的影响。对数控车床热误差进行补偿可以有效地提高机床的加工精度。在数控车床的加工过程中,采用铂电阻温度传感器对数控加工中关键点的温度进行实时测量,再配合线性回归理论建立数控车床的热误差模型。最后根据热误差模型对数控车床的加工误差进行实时补偿,经验证该技术是可靠有效的。     

高速电主轴热误差实验与预测建模

数控加工中心采用高速电主轴,由于电主轴发热导致主轴工作端明显热延伸,严重影响加工精度。以数控精雕机用永磁同步电主轴为研究对象,通过建立热特性实验平台,测试电主轴在不同转速、不同工作条件下的特征温度和热误差数据,建立基于自然指数函数的电主轴轴向热误差预测模型。在不同的工况下对模型的补偿结果进行实验验证,验证结果表明:该预测模型简单、精度较高,且建模成本较低,可以快速应用到实际的加工环境中。     

基于SSA-BP的电主轴热误差优化建模

为解决某加工中心电主轴的热误差补偿问题，建立预测精度高、鲁棒性强的热误差补偿模型。搭建实验台，利用美国雄狮回转误差分析仪采集电主轴的温度场和热误差数据。介绍麻雀搜索算法(SSA)原理、具体优化流程。采用SSA优化BP神经网络的权值和阈值，建立SSA-BP神经网络预测模型。与之前建立的BP神经网络预测模型相比，优化后预测效果更优，为电主轴热误差建模提供新的思路。     

Simulation of thermal behavior of a CNC machine tool spindle

The thermal deformations of a CNC machine tool spindle are the major contributor of thermal error. It is very significant both theoretically and practically to study how to accurately simulate the thermal error of the spindle. Firstly, this paper proposes a method for computing the coefficient of convection heat transfer of the spindle surface by referencing the theory on computing the coefficient of convection heat transfer of a flat plate when air flows along it. Secondly, the temperature field and thermal errors of the spindle are dynamically simulated under the actions of thermal loads using the finite element method. Thirdly, the characteristics of heat flow and thermal deformation within the spindle are analyzed according to the simulation results. Fourthly, the selection principle of thermal key points, which are indispensable for building a robust thermal error model, is provided based on the thermal error sensitivity technology. At last, a verification experiment is implemented on a CNC turning center, and the results show the simulation results are satisfying to replace the experiment results for further studies.     

卧式加工中心热变形控制与热误差补偿技术研究

从减少发热、控制温升、热误差补偿3个层次对卧式加工中心产品的热变形控制与热误差补偿技术进行了研究。第一层次从高精度功能部件的选用和润滑技术两方面研究减少机床的发热;第二层次从主轴温升控制、中空丝杠温升抑制、结构基础件温度控制等方面研究了冷却在控制关键部件的温升和均衡温度场中的作用;第三层次从数据采集、分析、建模等方面研究了热误差补偿技术。提出的各个层次控制热变形及热误差的具体技术和措施在实际产品中获得了应用,取得了优良的应用效果。     

数控机床主轴热误差的数据驱动模型研究

当实际工况与建模工况存在差异时,传统的热误差模型往往表现出较差的鲁棒性和预测精度,主要原因在于建模数据的局限性和模型的未建模动态。为了改善上述状况,提出了一种基于数据驱动的数控机床主轴补偿模型。此模型采用无模型自适应控制算法建模,结合机床运行中生成的数据(温度数据和误差数据)对热误差模型进行实时修正,使模型能快速适应新的加工工况,从而提高模型的鲁棒性。在一台数控车床主轴上进行了试验验证,结果表明:无模型自适应控制与多元回归模型比较,其标准差、最大残差和误差平方和分别提高了41%、62%和56%,此模型的鲁棒性和预测效果好。同时,此方法为大数据在机床主轴热误差补偿中的应用奠定了基础。     

基于FCM聚类和RBF神经网络的机床热误差补偿建模

热关健点的选择和热误差建模技术是决定热误差补偿是否有效的关键,对提高数控机床的加工精度至关重要.为了实现对数控机床热误差的补偿控制,文章利用模糊C均值(FCM)聚类方法,对机床上布置的温度测点进行优化筛选,将温度变量从20个减少到4个,然后给出了基于RBF热误差补偿建模方法.通过建模实例表明,文章提出的建模方法,在保证补偿模型精度的同时有效减少了温度测点,降低了变量耦合影响,并提高了补偿模型的鲁棒性.     

基于热模态分析的热误差温度测点优化选择

针对影响机床热误差建模的温度场分布问题,提出一种热模态分析方法,对机床热误差建模温度测点进行优化选择。以数控机床主轴温度场分析为例,利用热模态方法得到主轴各模态的时间常数、温度场及热变形模态形状,从而确定温度测点的最优位置。并通过实验验证了所建立模型的准确性与鲁棒性。     

加工中心主轴热误差实验分析与建模

以TH6350卧式加工中心为对象，构建了一套基于虚拟仪器系统的温度场和主轴的各项热误差。运用多元回归分析方法建立了加工中心主轴的热误差模型，采用模型聚类分析方法和逐步回归方法对模型进行了分析和优化，并提出了基于PC的误差补偿策略。     

Adaptive compensation of quasi-static errors for an intrinsic machine

An adaptive compensation strategy for quasi-static error correction in intrinsic machines is proposed and tested. The proposed methodology consists of systematic modelling of the machine forward kinematics, including quasi-static errors, as well as direct modelling of the inverse kinematics using nonlinear regression analysis. The result is a model which is a hybrid of physical modelling and regression analysis modelling. In addition, the methodology includes a compensation strategy of the machine contouring errors using the state observer technique for on-line adaptive compensation. A CMM is chosen as a test bed for validation of the proposed methodology. Systematic modelling is carried out in two stages for the forward and inverse kinematics. Regression based models are verified using two different tests. The statistical analysis of variance technique (ANOVA) is used to select the best model in addition to model testing using an independent set equal to approximately 10% of the fitting data. The obtained models are then employed in two compensation strategies; one for the measurement error correction, and another one for the contouring error correction by motion command modification in the forward control path. For contouring tests, the CMM behavior at different thermal states is estimated using experimentally obtained Effective Coefficient of Thermal Expansion (ECTE). Simulations of the machine in contouring selected trajectories are carried out over a range of thermal states. Results obtained show an improvement in the CMM performance to a level close to the machine resolution. The CMM performance is tested using the standard ASME B.89.1.12M-1990 evaluation test, as well as a novel modified version of the test accounting for a thermally varying environment. Machine errors are significantly reduced using the proposed methodology.     

Dynamic neural network modeling for nonlinear, nonstationary machine tool thermally induced error

This paper presents a new modeling methodology for nonstationary machine tool thermal errors. The method uses the dynamic neural network model to track nonlinear time-varying machine tool errors under various thermal conditions. To accommodate the nonstationary nature of the thermo-elastic process, an Integrated Recurrent Neural Network (IRNN) is introduced to identify the nonstationarity of the thermo-elastic process with a deterministic linear trend. Experiments on spindle thermal deformation are conducted to evaluate the model performance in terms of model estimation accuracy and robustness. The comparison indicates that the IRNN performs better than other modeling methods, such as, multi-variable regression analysis (MRA), multi-layer feedforward neural network (MFN), and recurrent neural network (RNN), in terms of model robustness under a variety of working conditions.     

机床热变形及热误差优化研究

为减少热变形对精密加工精度影响,对夏冬两季节机床主轴箱上温升和热变形及环境温度的影响进行了测试分析,并采用BP神经网络模型化的Volterra级数非线性系统实现热误差建模。分析结果表明：夏天环境温度受主轴箱散热影响而温度迅速升高;冬季机床散热较快,主轴箱上温升比较明显,环境温度几乎不变;同一台机床在夏季和冬季的热变形规律相似而变形量稍有不同。通过实验验证了该模型具有预测精度高的优点,为数控机床热误差实时补偿提供了参考。     

Thermal error measurement and modelling in machine tools.: Part I. Influence of varying operating conditions

Thermal error in machine tools has been observed to be closely linked to the temperature of critical elements of the machine. It was found, in the experiments conducted herein, that there was a significant increase in the axis positioning error on account of an increase in the temperature of the machine elements due to continuous operation. However, it was also observed that the specific operating parameters of the test cycles carried out also significantly affected the positioning error. Different sets of operating parameters generated significantly different error values even though the temperature of the machine elements generated by those operating conditions was similar. As such, this observation forms a very important consideration in the development of a generic thermal error compensation system. It appears to indicate that such a system needs to be capable of evaluating the compensation depending upon the temperature values of the machine elements as well as account for the effect that different operating parameters induce upon the positioning error under the same thermal condition of the machine. This paper attempts to analyse the thermal behaviour of a three-axis vertical machining centre under the influence of various operating parameters and, through the experimental results obtained, point out and explain the effect of these parameters on the axis positioning error. This behaviour forms the basis of an improved modelling methodology that is presented separately in Part II of this paper.     

基于CS-GMC(1,N)的数控机床全新热误差建模优化方法

为降低机床热误差对数控加工精度的影响，提高灰色模型GMC(1,N)的预测精度，将布谷鸟搜索（CS）算法引入GMC(1,N)灰色模型中，用于优化灰色模型GMC(1,N)的生成系数，构建了基于CS-GMC(1,N)的数控机床热误差预测模型。以小型三轴立式数控铣床为研究对象进行了主轴热误差实验，热误差预测性能分析结果表明：CS-GMC(1,N)模型的预测精度高于PSO-GMC(1,N)模型，为机床主轴热误差建模及后续热误差补偿提供了参考。     

数控机床的热误差建模与补偿研究

数控机床的热变形误差是影响其加工精度的主要因素。针对当前机床热误差难以解决的问题,提出一种融合模糊聚类理论、灰色关联理论和多元线性回归理论的热误差建模及补偿原理,通过应用于实验室自主研制的AGPM,经机床温度场的测点优化分析、多元线性回归求解,建立了精确的热误差补偿模型。经补偿验证,该原理理论正确、简单易行、稳定可靠,可以大幅减小机床的热变形误差。     

基于神经网络系统的滚珠丝杠热误差建模

在追求高精度加工的现代数控系统中,热误差的消除具有重要的意义.文章首先简述了神经网络系统的特性及训练方法,成功地将神经网络模型应用于对数控机床直线进给系统的热误差进行建模,并取得了预期的成果,使最大预测误差降低到2μm,为进一步的热误差补偿奠定了基础.详细阐述了实际建模流程,根据训练数据的具体特征提出了一种新的数据预处理方法,使这些数据能更有效地应用于模型训练,是论文的一个创新点.     

数控机床热变形误差补偿技术

热变形误差是影响机床加工精度的重要因素之一,通过实时热变形误差补偿可以提高数控机床加工精度.本文在分析产生机床热误差的原理的基础上, 探讨了热误差的测量方法,利用多元线性回归方法建立了机床热变形与温升之间的数学模型.应用数控系统的PLC补偿功能,对XH178加工中心加工过程中的热误差进行了实时补偿.实验结果表明误差补偿量达到80%以上.     

Characterizations and models for the thermal growth of a motorized high speed spindle

In this paper, the characterizing and modeling of the thermal growth of a motorized high speed spindle is reported. A motorized high speed spindle has more complicated dynamic, non-stationary and speed-dependent thermal characteristics than conventional spindles. The centrifugal force and thermal expansion occurring on the bearings and motor rotor change the thermal characteristics of the built-in motor, bearings and assembly joints. It was found that conventional static models using regression analysis and artificial neural network failed to give satisfactory model accuracy and robustness. An auto-regression dynamic thermal error model, that considers the temperature history and spindle-speed information, has been proposed and proved to improve the model accuracy. However, it was found that temperature-based thermal error models, that correlated thermal displacement of the rotating cutting tool to the temperature measurements on the spindle housing, were not robust. Many nonlinear and time-varying thermal sources, such as coolant jacket, motor air gap, motion joints and assembly interfaces influence thermal displacement. The relationship between temperature measurements and thermal displacements is highly nonlinear, time-varying and non-stationary. A new thermal model which correlates the spindle thermal growth to thermal displacements measured at some locations of the rotating spindle shaft has been proposed. It was found that the displacement-based thermal error model has much better accuracy and robustness than the temperature-based model.