Application of projection pursuit regression to thermal error modeling of a CNC machine tool

Thermal errors are the major contributor to the dimensional errors of a workpiece in precision machining. Error compensation technique is a cost-effective way to reduce thermal errors. Accurate modeling of errors is a prerequisite of error compensation. In this paper, a thermal error model was proposed by using projection pursuit regression (PPR). The PPR method improves the prediction accuracy of thermal errors in the computer numerical control (CNC) turning center. A thermal error compensation system was developed based on the PPR model, and which has been applied to the CNC turning center in daily production. The results show that the thermal drift in workpiece diameter has been reduced from 34 to 5???m.     

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。     

数控机床工作台误差综合补偿方法研究

针对由几何误差与热误差引起的数控机床工作台与主轴之间相对位置变动的问题,通过试验分析其在不同温度状态下的误差数据,得到机床工作台平面度误差随热变形保持不变的规律,并提出了一种数控机床工作台平面度误差与主轴热误差的综合补偿方法。该方法通过分别建立工作台平面度误差模型和热误差模型,并运用叠加原理建立综合误差补偿模型,对传统固定单位置点建模补偿方法的原理性缺陷进行了改进。结合机床关键部件的实时温度值和刀具位置的实时坐标值,计算出了全工作台各区域各温度阶段的误差补偿值,进而实现了全工作台主轴轴向综合误差的实时补偿。检验及分析结果表明,相比于传统固定单位置点热误差建模补偿方法,该方法所建模型残余标准差减小约7μm,精度提高比例达到50%;单次最大补偿残差减小约11μm,精度提高比例达到60%,大幅度提高了机床的加工精度。     

大型数控龙门铣床几何误差补偿方法研究

针对大型数控龙门铣床几何误差的问题,建立了大型数控龙门铣床的几何误差模型,分析了大型数控龙门铣床的几何误差源;利用API(T3)激光跟踪仪高精度大尺寸的测量特点及数据处理能力,提出了X、Y、Z轴线位移误差、角位移误差及各轴间垂直度误差的辨识算法,通过激光测量与计算准确地辨识了大型数控龙门铣床的几何误差;建立了大型数控龙门铣床加工空间几何误差数学模型,采用基于对象的事件驱动机制的程序设计语言Visual Basic开发了几何误差补偿软件,实现了几何误差补偿;现场检测了大型数控龙门铣床空行程平面运动轨迹及工件的平面度。研究结果表明,该方法使平面加工精度提高了50.77%,并验证了几何误差模型的正确性及几何误差补偿方法的有效性。     

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。     

数控机床全误差模型和误差补偿技术的研究

加工精度是数控机床必须保证的一项性能指标。提高机床精度是先进制造技术的重要课题，有误差避免和误差补偿两种方法。前者使机床造价大幅上升，而且精度的提高也有一定的限度。后者的精度提高几乎没有限制，对数控机床，计算机实时误差补偿技术是一种经济、有效的基本途径。基于多体系统理论，推导了多坐标数控机床，包含几何误差和热误差的全误差模型。文中介绍了坐标数控机床项误差的辨识方法(22线、14线和9线法)，还介绍了回转坐标6项误差的辨识方法。通过软件补偿，在3坐标联动和4坐标联动数控机床上实现了几何误差和热误差的补偿。实践结果表明误差模型的准确性和补偿方法的实用性。     

Application of ACO-BPN to thermal error modeling of NC machine tool

Thermal errors are the major contributor to the dimensional errors of a workpiece in precision machining. Error compensation technique is a cost-effective way to reduce thermal errors. Accurate modeling of errors is a prerequisite of error compensation. In this paper, four key temperature points of a NC machine tool were obtained based on clustering method. A thermal error model based on the four key temperature points was proposed by using ant colony algorithm-based back propagation neural network (ACO-BPN). The ACO-BPN method improves the prediction accuracy of thermal deformation in the NC machine tool. A thermal error compensation system was developed based on the proposed model, and which has been applied to the NC machine tool in daily production. The results show that the thermal drift in workpiece diameter has been reduced from 33 to 8 μm from its center of tolerance.     

Neural network-based modelling and error compensation of thermally-induced spindle errors

This research is concerned with enhancing the accuracy of a machining centre by compensating for thermally induced spindle errors in real-time. A neural network model was developed for on-line thermal error monitoring. A PC-based error compensation scheme was also developed to upgrade a commercial CNC controller for real-time thermal error compensation without any hardware modifications to the machine. The spindle thermal errors of a vertical machining centre were reduced by 70% after compensation.     

基于状态空间模型的数控机床热误差建模

为了减小机床运行参数变化导致的机床热误差变化对模型预测精度的影响,提出了状态空间建模算法,该算法可根据机床运行参数的变化而自动调整模型,从而使模型对机床运行参数的变化具有良好的自适应性。通过实验比较了模型对机床处于不同条件下的热误差预测精度,并基于状态空间模型在Leaderway V-450型数控机床上进行了平面切削的热误差补偿实验。实验结果表明：与传统热误差建模算法相比较,所提算法的预测精度提高了58.12%,稳健性也得到了有效提升,且实际热误差补偿效果显著。     

Enhancing positioning accuracy of CNC machine tools by means of direct measurement of deformation

The positioning accuracy of computer numerical control (CNC) machine tools is mainly limited by the manufacturing accuracy of their linear and circular motion axes and by the long-term dimensional stability of their structures. Maximizing this accuracy can prove to be a particularly challenging task, especially for large-sized systems. In fact, heat-induced deformations, long-period deformation of foundations and the manufacturing process itself, these all cause time-dependent structural deformations of the machine body, which are difficult to model and to predict. The usual approach is a model-based prediction of structural deformations, which is followed by a compensation of positioning errors at CNC level. This approach is often limited by the complexity of the problem from both geometrical (system geometry can be very complex and it can vary in time) and physical (it is difficult to model and consider any possible load type and loading condition) point of view. As a consequence, only limited success has been achieved in active error compensation based on the modelling of the relationship between the generalized dynamic loads and the structural deformation field. This paper illustrates a different approach in active error compensation, which exploits a new measurement system able to provide real-time measurement of the displacement field of a given structural component, without any model about its dynamic/thermal structural behavior.     

Thermal error analysis,modeling and compensation of five-axis machine tools

The role of five-axis CNC machine tools (FAMT) in the manufacturing industry is becoming more and more important, but due to the large number of heat sources of FAMT, the thermal error caused by them will be more complicated. To simplify the complicated thermal error model, this paper presents a new modelling method for compensation of the thermal errors on a cradle-type FAMT. This method uses artificial neural network (ANN) and shark smell optimization (SSO) algorithm to evaluate the performance of FAMT, and developing the thermal error compensation system, the compensation model is verified by machining experiments. Generally, the thermal sensitive point screening is performed by a method in which a large number of temperature sensors are arranged randomly, it increases the workload and may cause omission of the heat sensitive point. In this paper, the thermal imager is used to screen out the temperature sensitive points of the machine tool (MT), then the temperature sensor is placed at the position of the heat sensitive point of the FAMT, and the collected thermal characteristic data is used for thermal error modeling. The C-axis heating test, spindle heating test, and the combined movement test are applied in this work, and the results show that the shark smell optimization artificial neural network (SSO-ANN) model was compared to the other two models and verified better performance than back propagation artificial neural network (BP-ANN) model and particle swarm optimization neural network (PSO) model with the same training samples. Finally, a compensation experiment is carried out. The compensation values, which was calculated by the SSO-ANN model are sent to the real-time error compensation controller. The compensation effect of the model is then tested by machining the ‘S’-shaped test piece. Test results show that the 32 % reduction in machining error is achieved after compensation, which means this method improves the accuracy and robustness of the thermal error compensation system.

     

A new high-efficiency error compensation system for CNC multi-axis machine tools

To enhance the accuracy of CNC machines for the request of modern industry, an effective static/quasi-static error compensation system composed of an element-free interpolation algorithm based on the Galerkin method for error prediction, a recursive software compensation procedure, and an NC-code converting software, is developed. Through automatically analyzing the machining path, the new error prediction method takes into consideration the fact that the machine structure is non-rigid, and can efficiently determine the position errors of the cutter for compensation without computing a complex error model on-line. The predicted errors are then compensated based on a recursive compensation algorithm. Finally, a compensated NC program will be automatically generated by the NC-code converting software for the precision machining process. Because of the advantage of the element-free theory, the error prediction method can flexibly and irregularly distribute nodal points for accurate error prediction for a machine with complex error distribution characteristics throughout the workspace. To verify the algorithm and the developed system, cutting experiments were conducted in this study, and the results have shown the success of the proposed error compensation system.     

基于外部机床坐标系偏移的热误差实时补偿

基于数控系统的外部机床坐标系偏移功能，通过修改数控系统中的PLC程序，将数控机床的热变形误差，即工件与刀具间的相对热运动值读入数控系统，利用外部机床坐标系的偏移而实现热误差的实时补偿，开发研制了高精度、低成本、满足实际要求的热误差实时补偿系统。经实际生产应用，机床的加工精度得到了大幅度提高。     

基于遗传算法优化小波神经网络数控机床热误差建模

数控机床的热误差已经成为影响其加工精度的一个关键因素,为最大限度提高数控机床热误差补偿的精度和效率,结合遗传算法自适应全局优化搜索能力和小波神经网络良好的时频局部特性的优点,提出一种基于遗传算法优化小波神经网络的机床热误差补偿模型。以某型号五轴摆动卧式加工中心为试验对象,以机床温度变量和热误差为数据输入样本,建立小波神经网络模型热误差预测模型,然后用遗传算法优化小波神经网络权值、阈值,最终建立热误差预测模型。通过与传统人工神经网络和普通小波神经网络进行对比分析及试验论证表明,该补偿模型具有精度高、抗扰动能力和鲁棒性强等优点,有望在实际加工场合的数控机床的热误差预测和补偿研究中得到更大的推广应用。     

数控机床主轴热变形伪滞后研究及主轴热漂移在机实时补偿

为研究数控机床热变形规律,实现数控机床误差在机实时补偿,进行数控机床主轴热变形理论及试验分析,结果表明,数控机床主轴热变形与主轴温变在距热源约1/3位置存在近似线性关系,即主轴热变形存在伪滞后现象,这一结果为数控机床测温点优化布置及热误差鲁棒建模提供理论依据。为验证机床热变形伪滞后现象,对VM850加工中心主轴热漂移误差在机实时检测并建模,通过自主研发数控机床误差在线实时补偿系统对主轴热漂移误差进行实时补偿,经补偿,机床主轴热漂移误差减少90%以上,有效提高了数控机床主轴精度。     

五轴数控机床几何误差建模方法研究

五轴数控机床是实现工件复杂表面精密加工的重要设备,而机床本身精度是保证加工精度的重要前提。以一台大型五轴数控加工机床为研究对象,分析各项误差,应用多体系统运动学理论,建立移动轴与旋转轴的几何误差数学模型,推导出刀具相对工件坐标系的位置与姿态误差表达式,为误差补偿提供精确数学模型,提高机床加工精度。     

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。     

考虑热误差的双转台机床工艺误差谱预测方法

五轴数控机床的几何误差和热误差是影响工件加工精度的两个重要因素,对这些误差因素进行分析可以有效提高薄壁件工件的加工精度。本文首先基于齐次坐标变换法,建立了双转台五轴数控机床的旋转轴几何误差模型;然后基于对标准球进行在机接触测量,辩识得出两旋转轴的12项几何误差,这些误差考虑了两旋转轴之间的相互影响和其热误差的影响;最后分析五轴数控机床加工空间的几何误差场,在该加工空间内几何误差从中心到外侧逐渐增加,当A轴旋转角度增加时,误差的最大值也随之增加。与其它位置误差辨识方法相比,本方法的测量精度符合加工要求,测量时间只需要30 min。     

Experimental implementation of Taylor series expansion error compensation on a bi-axial CNC machine

In this paper, a new model-based Taylor series expansion error compensation (TSEEC) strategy is proposed to improve the contouring accuracy for computer numerically controlled (CNC) machines. In TSEEC, the contour error compensation problem is formulated as a Taylor series expansion problem, in which the value of the contour error is expanded around the reference points and the compensation components are calculated as the deviations from the reference points. Simulations show that, with perfect knowledge of the axial dynamics, zero contour errors can be achieved with TSEEC for both linear and circular contours. Due to modeling errors, external disturbances, and measurement noise, some modifications and experimentation need to be made to determine suitable parameters for implementation of the TSEEC scheme on a real machine. These measurements include a low-pass filter, a choice of a compensation target, and a compensation gain. Experimental results show the effectiveness of TSEEC in reducing contour errors and demonstrate the superiority of TSEEC over inverse feedforward compensation and cross-coupled control in improving the contouring accuracy.     

Robustness of thermal error compensation modeling models of CNC machine tools

In order to achieve effective control of thermal error compensation of computer numerical control (CNC) machine tools, the prediction accuracy and robustness of the compensation model is particularly important. In this paper, the temperature of sensitive points and thermal error of the spindle in Z direction are measured. Using a combination of fuzzy clustering analysis and gray correlation method to select temperature-sensitive points and then using multiple linear regression of least squares and least absolute estimation methods, distributed lag model, and support vector regression machine to establish prediction models of the relationship between temperature of sensitive points and the thermal error. Also, the temperature values of sensitive points and the thermal error in the experimental conditions of different ambient temperatures and different spindle speeds are measured. By comparing the prediction accuracy of various prediction models under different experimental conditions verify the robustness of the models. Experimental results show that when the modeling data are less, the prediction accuracy of multiple linear regression of least squares and least absolute estimation methods and distributed lag model is declined, and their robustness are poor, while support vector regression model has good prediction accuracy and its robustness remains strong when changing the experimental conditions. However, when modeling data are rich, the prediction accuracy of various algorithms is improved, but the robustness of support vector regression model is volatile. The robustness analysis of different models provides a useful reference for the thermal error compensation model, selection of CNC machine tools, and has good engineering applications.