Thermal Error Compensation of the Wear-Depth Real-Time Detecting of Self-Lubricating Spherical Plain Bearings

The spherical plain bearing test bench is a necessary detecting equipment in the research process of self?lubricating spherical plain bearings. The varying environmental temperatures cause the thermal deformation of the wear?depth detecting system of bearing test benches and then a ect the accuracy of the wear?depth detecting data. However, few researches about the spherical plain bearing test benches can be found with the implementation of the detect?ing error compensation. Based on the self?made modular spherical plain bearing test bench, two main causes of ther?mal errors, the friction heat of bearings and the environmental temperature variation, are analysed. The thermal errors caused by the friction heat of bearings are calculated, and the thermal deformation of the wear?depth detecting sys?tem caused by the varying environmental temperatures is detected. In view of the above results, the environmental temperature variation is the main cause of the two error factors. When the environmental temperatures rise is 10.3 °C, the thermal deformation is approximately 0.01 mm. In addition, the comprehensive compensating model of the thermal error of the wear?depth detecting system is built by multiple linear regression(MLR) and time series analysis. Compared with the detecting data of the thermal errors, the comprehensive compensating model has higher fitting precision, and the maximum residual is only 1 μm. A comprehensive compensating model of the thermal error of the wear?depth detecting system is proposed, which provides a theoretical basis for the improvement of the real?time wear?depth detecting precision of the spherical plain bearing test bench.

Thermal Error Compensation for Telescopic Spindle of CNC Machine Tool Based on SIEMENS 840D System

In this paper, eddy current sensors and thermocouple sensors were employed to measure the thermal field and thermal deformation of a spindle of a telescopic CNC boring-milling machine tool, respectively. A linear regression method was proposed to establish the thermal error model. Furthermore, two compensation methods were implemented based on the SIEMENS 840D system by using the feed shaft of z direction and telescopic spindle respectively. Experimental results showed that the thermal error could be reduced by 73.79% when using the second compensation method, and the thermal error could be eliminated by using the two compensation methods effectively.

Modeling Approach of Regression Orthogonal Experiment Design for Thermal Error Compensation of CNC Turning Center

The thermal induced errors can account for as much as 70% of the dimensional errors on a workpiece. Accurate modeling of errors is an essential part of error compensation. Base on analyzing the existing approaches of the thermal error modeling for machine tools, a new approach of regression orthogonal design is proposed, which combines the statistic theory with machine structures, surrounding condition, engineering judgements, and experience in modeling. A whole computation and analysis procedure is given. Therefore, the model got from this method are more robust and practical than those got from the present method that depends on the modeling data completely. At last more than 100 applications of CNC turning center with only one thermal error model are given. The cutting diameter variation reduces from more than 35 μm to about 12 μm with the orthogonal regression modeling and compensation of thermal error.

IMPROVING ACCURACY OF CNC MACHINE TOOLS THROUGH COMPENSATION FOR THERMAL ERRORS

A method for improving accuracy of CNC machine tools through compensation for the thermal errors is studied. The thermal errors are obtained by 1 D ball array and characterized by an auto regressive model based on spindle rotation speed. By revising the workpiece NC machining program, the thermal errors can be compensated before machining. The experiments on a vertical machining center show that the effectiveness of compensation is good.

Periodic Error Compensation for Quartz MEMS Gyroscope Drift of INS

In order to improve the navigation accuracy of an inertial navigation system （INS）, composed of quartz gyroscopes, the existing real-time compensation methods for periodic errors in quartz gyroscope drift and the periodic error term relationship between sampled original data and smoothed data are reviewed. On the base of the results, a new compensation method called using former period characteristics to compensate latter smoothness data （UFCL for short） method is proposed considering the INS working characteristics. This new method uses the original data without smoothing to work out an error conversion formula at the INS initial alignment time and then compensate the smoothed data errors by way of the formula at the navigation time. Both theoretical analysis and experimental results demonstrate that this method is able to cut down on computational time and raise the accuracy which makes it a better real-time compensation approach for periodic error terms of quartz micro electronic mechanical system （MEMS） gyroscope＇s zero drift.

CNC Thermal Compensation Based on Mind Evolutionary Algorithm Optimized BP Neural Network

Thermal deformation error is one of the most important factors affecting the CNCs’ accuracy, so research is conducted on the temperature errors affecting CNCs’ machining accuracy;on the basis of analyzing the unpredictability and pre-maturing of the results of the genetic algorithm, as well as the slow speed of the training speed of the particle algorithm, a kind of Mind Evolutionary Algorithm optimized BP neural network featuring extremely strong global search capacity was proposed;type KVC850MA/2 five-axis CNC of Changzheng Lathe Factory was used as the research subject, and the Mind Evolutionary Algorithm optimized BP neural network algorithm was used for the establishment of the compensation model between temperature changes and the CNCs’ thermal deformation errors, as well as the realization method on hardware. The simulation results indicated that this method featured extremely high practical value.

Numerical Investigation of Thermal Behavior of CNC Machine Tool and Its Effects on Dimensional Accuracy of Machined Parts

The dimensional accuracy of machined parts is strongly influenced by the thermal behavior of machine tools (MT). Minimizing this influence represents a key objective for any modern manufacturing industry. Thermally induced positioning error compensation remains the most effective and practical method in this context. However, the efficiency of the compensation process depends on the quality of the model used to predict the thermal errors. The model should consistently reflect the relationships between temperature distribution in the MT structure and thermally induced positioning errors. A judicious choice of the number and location of temperature sensitive points to represent heat distribution is a key factor for robust thermal error modeling. Therefore, in this paper, the temperature sensitive points are selected following a structured thermomechanical analysis carried out to evaluate the effects of various temperature gradients on MT structure deformation intensity. The MT thermal behavior is first modeled using finite element method and validated by various experimentally measured temperature fields using temperature sensors and thermal imaging. MT Thermal behavior validation shows a maximum error of less than 10% when comparing the numerical estimations with the experimental results even under changing operation conditions. The numerical model is used through several series of simulations carried out using varied working condition to explore possible relationships between temperature distribution and thermal deformation characteristics to select the most appropriate temperature sensitive points that will be considered for building an empirical prediction model for thermal errors as function of MT thermal state. Validation tests achieved using an artificial neural network based simplified model confirmed the efficiency of the proposed temperature sensitive points allowing the prediction of the thermally induced errors with an accuracy greater than 90%.

Identification of the key thermal points on machine tools by grouping and optimizing variables

The grouping and optimization approach to identify the key thermal points on machine tools is studied.To solve the difficulty in grouping because of the high correlated variables from distinct groups,the variables grouping technique is improved.Temperature variables are sorted according to their relativities with the thermal errors.The representative temperature variables are determined by analyzing the variable correlation in sort order and removing the other variables in the same group.Considering the diverse effect of importing the different variables on thermal error model,the method of variable combination optimization is improved.Regression models made up of different combination of representative temperature variables are evaluated by the index of both the determined coefficient and the average residual squares to select the combination of the temperature variables.For the machine tools with complicated structures which need more initial temperature measuring points the improvement is demanded.The improved approach is applied to a precision horizontal machining center to identify the key thermal points.Experimental results show that the proposed approach is capable of avoiding the high correlation among the different groups' variables,effectively reducing the number of the key thermal points without depressing the prediction accuracy of the thermal error model for machine tools.

近红外光谱传感器的测点热误差补偿方法设计

红外光谱传感器测量受测点不合理产生的热误差影响存在测量偏差较大的问题。提出一种近红外光谱传感器测点热误差补偿方法。根据近红外光谱传感器热变形和测点温度之间的关联性,确定近红外光谱传感器最佳测温点。根据偏F统计量的检验在回归模型中加入拟合处理后的温度变量,删除前期无利用价值的温度变量,以此为依据建立测点热误差模型。根据测点热误差模型中的约束条件,对各个测点进行标定处理,同时引入人工神经网络对近红外光谱传感器各个测点进行温度拟合补偿。实验结果表明,所提方法得到的测点热误差补偿量最高值为45μm、对丝杠XYZ三个轴的补偿量与实际值一致、补偿后的误差控制在4.5μm以内。证明采用所提方法可以获取满意的近红外光谱传感器温度补偿结果。

大型射电望远镜日照热误差及其补偿的仿真研究

射电望远镜广泛应用于射电天文、测控导航等领域,随着其工作频率的升高、口径的增大,日照引起的结构热变形对其性能的影响愈发严重;本文针对待建的新疆110 m口径射电望远镜,建立了其日照热力耦合模型,分析了天线在不同时刻、风速及姿态下的温度、变形情况,总结了结构日照温度场、变形场的时空分布特征,最后采用最佳吻合抛物面方法对反射面精度进行了评价,并通过副反射面位置补偿量的变化趋势揭示了天线热变形的共性规律及机理。结果表明:对于日照引起的天线热变形误差,风速越大,结构温度分布越均匀,其越接近等温膨胀变形,反射面形状精度越高;温差引起的结构不均匀变形是反射面精度下降的主要原因,温差越大结构的不均匀变形越大,反射面的形状精度越低;同一姿态不同风速下反射面热误差空间分布具有相似性,该分布与太阳直射点位置直接相关,且会跟随直射点位置发生变化,但由于日照强度、风速等因素的不同,其变形幅度不同。各种姿态下反射面精度变化规律相似,与风速的相关性均为反射面精度随风速上升而提高;采用副反射面位置补偿技术可明显缓解日照热误差影响。本文的分析方法与结论对大口径全可动射电望远镜的设计建造及其热误差控制具有一定参考价值。

数控机床几何与热误差研究方法综述

数控机床加工精度是衡量机床性能的关键指标。影响加工精度的误差源有多种,最显著的是几何误差与热误差,占据机床总误差的60%~70%。误差补偿被认为是提高机床加工精度经济高效的手段,其中误差精确测量、误差模型的建立是进行误差补偿的基础和前提。针对数控机床综合误差的测量、建模、补偿过程进行归纳总结,分析测量工具、建模和补偿方案的优缺点,列举数控机床精度优化方面的问题,并探讨未来的研究方向。

CNC机床丝杠热误差实时补偿设计及自动补偿试验

为了降低数控机床丝杠传动系统因热误差引起的定位误差,完成机床工作台各运行阶段的实时补偿。用非接触模式为丝杠非电机连接端安装位移检测器,以电涡流传感器时间监测丝杠端面位置数据,获得丝杠产生的总热误差,通过热补偿得到每段丝杠的热误差程度,确定每段坐标系原点发生的偏移,完成自主补偿机床丝杠热误差的效果。研究结果表明:采用分段补偿方法可以获得比其他热误差补偿模型更优的丝杠全段补偿性能。分别检测丝杠各段发生的热误差再对其实施补偿,可以根据丝杠各部位热误差程度实施补偿。

非球面数控磨床热误差建模分析与补偿研究

文献调研了占比(40~70)%的热误差是影响数控机床加工精度的最主要因素,探讨了研究对象非球面数控磨床的结构特征,分析了非球面数控磨床热误差存在的基本形式,提出了基于统计学理论的多元线性回归模型算法进行热误差建模的具体方案,针对非球面数控磨床磨轮轴轴向热误差进行了多元线性回归建模,并构建了热误差补偿系统,实验表明,研究的基于多元线性回归模型算法和热误差补偿系统可以有效的实施非球面数控磨床磨轮轴轴向热误差补偿,补偿后残差方差降至4.216e-002,可以满足非球面数控磨床精密磨削需求。

距离和雾对红外测温精度影响的补偿研究

为提高在线式红外热像仪在大雾天气下的测温精度,研究了距离、相对湿度和雾对红外热像仪测温精度的影响。采用二次开发的热红外故障数据采集系统,搭建实验平台,分别进行单因素和多因素干扰下的测温实验。得到距离-误差温度的分段多项式拟合关系;基于暗通道先验理论,实现对雾的定量描述,得到透射率-误差温度的指数函数拟合关系;以代数和的方式,提出一种误差补偿模型来补偿距离和雾共同作用产生的测量误差。实验结果表明,该模型能显著提高热像仪测温精度,对在线式红外热像仪在大雾环境下进行长时间精确温度数据的采集与存储,构建设备故障数据特征库具有重要意义。

基于热误差补偿的行星滚柱丝杠综合性能试验台设计与分析

行星滚柱丝杠综合性能是丝杠性能的主要指标,现有测试系统多以静态测量法进行测量,操作复杂且偶然误差较高。通过对行星滚柱丝杠综合性能动态测试系统的试验原理、硬件设计、软硬件通讯、软件界面结构的研究,设计了一套行星滚柱丝杠综合性能动态测试系统。基于LabVIEW设计上位机人机交互与控制监测软件对丝杠热伸长量进行了检测,并使用最小二乘法分析丝杠温升与位移的关系,对定位精度试验进行误差补偿。实验结果表明,所设计的试验台系统可以测量行星滚柱丝杠定位精度、噪声、温升、极限转速、加速度,同时热误差测量补偿系统可以减小温度变化对测量位置的影响,补偿前后精度测量指标提升均在30%以上,偶然性误差较小并且精度提高。

机床热误差自适应学习补偿方法研究

机床主轴在运行过程中,受热容易产生变形,从而造成机床加工后的产品精度下降。对此,设计了机床热误差自适应学习补偿控制方法,并对补偿前和补偿后的效果进行实验验证。给出了机床热误差自适应控制原理图,提出了一种用于识别和更新热误差的外生输入自回归模型。采用加权最小二乘法进行参数自适应更新,从而对机床运行产生的热误差进行补偿。设定热误差的动作控制限值,通过实验检验补偿前和补偿后机床热误差变化结果。结果显示:补偿前,机床运行一段时间后,在X和Z轴方向上产生的热误差较大,都超过了误差的设置范围;补偿后,机床运行一段时间后,产生的热误差较小,都保持在设置范围内。采用自适应学习补偿控制方法,能够降低机床产生的热误差,从而提高机床对产品的加工精度。

数控机床几何误差与热误差综合建模及其实时补偿

为提高数控机床的精度,提出一种数控机床的几何与热的复合误差综合建模方法。通过分析机床在不同温度状态下的误差数据,得到机床误差分布规律;根据几何误差和热误差的不同特性进行误差分离,采用多项式拟合与线性拟合方法建立机床几何误差与热误差的综合数学模型;利用数控(Computer numerical control,CNC)系统的外部机床坐标系偏置功能,应用自行研发的综合误差实时补偿系统进行误差在线实时补偿。该误差补偿方法综合考虑机床几何误差及其在机床不同温度下的变化,全面分析整个温升过程直至热稳态的误差及其变化规律。经检测认证表明,应用该误差补偿方法及其实时补偿系统可使机床在常温下的定位误差由44.1μm降低到3.6μm,补偿91.8%;温升之后的定位误差由26.0μm降低到5.1μm,补偿80.4%,大幅度提高机床的精度。

龙门数控机床主轴热误差及其改善措施

依据ISO和ASME标准建立龙门数控(Numerical control,NC)机床热误差测试条件,通过主轴恒转速和变转速热误差试验分析主轴箱温度场分布及其对主轴热误差的影响趋势。建立龙门机床误差元素模型,分析影响机床各坐标轴加工精度的主轴热误差分量。研究发现,主轴热误差和主轴箱温度存在单调对应关系,温度对主轴轴向的热伸长误差的影响要远大于主轴径向的热漂移误差,但温度变化相对各坐标变形存在热延迟和热惯性等特性。对主轴径向精度影响最大的热误差分量是由机床生热产生的同方向的偏移误差和与之垂直的偏转误差;对轴向精度影响最大的则是轴向的偏移误差。针对热误差特点和分布规律,提出结构优化、热平衡、误差补偿建模等3种减小热误差的措施,并对其各自优点进行了分析。

基于RBF神经网络的数控车床热误差建模

对于数控车床而言 ,热误差是其最大的误差源 ,而其中最困难的是热误差建模 .现有 BP算法的神经网络模型存在学习收敛速度慢 ,容易陷入局部极小点的缺点 .文中使用径向基函数理论建立了基于 RBF神经网络的数控机床热误差数学模型 .讨论了 RBF网络参数的初始化及学习 ;给出了两种建模方式的 RBF网络建模算例 ,将其建模性能指标与经典最小二乘法建模指标进行综合对比 ,可知 RBF网络各项指标均优于经典最小二乘方法 .最后验证了 RBF网络建模的鲁棒性 .结果表明 :径向基神经网络模型与经典最小二乘线性模型相比 ,拟合性能更好 。

数控机床加工精度提高技术的进展及其存在的问题

数控技术和数控机床的诞生开创了控制和生产领域的新时代,给机械制造业带来了一次新的技术革命。数控机床技术的迅速进步大大推进了精密、超精密加工技术的发展,使加工精度提高到了一个新的台阶,提高数控机床精度的研究也得到了极大的重视。从误差防止和误差补偿两个方面,介绍了国内外关于提高数控机床精度的研究现状,指出了具有广泛应用前景的误差补偿这项实用技术之所以难以在国内数控机床行业普及和使用所存在的核心问题。最后根据企业实际情况提出开展误差补偿技术应用研究的必要性和主要研究内容。