Temperature Variable Optimization for Precision Machine Tool Thermal Error Compensation on Optimal Threshold

Machine tool thermal error is an important reason for poor machining accuracy. Thermal error compensation is a primary technology in accuracy control. To build thermal error model, temperature variables are needed to be divided into several groups on an appropriate threshold. Currently, group threshold value is mainly determined by researchers experience. Few studies focus on group threshold in temperature variable grouping. Since the threshold is important in error compensation, this paper arms to find out an optimal threshold to realize temperature variable optimization in thermal error modeling. Firstly, correlation coefficient is used to express membership grade of temperature variables, and the theory of fuzzy transitive closure is applied to obtain relational matrix of temperature variables. Concepts as compact degree and separable degree are introduced. Then evaluation model of temperature variable clustering is built. The optimal threshold and the best temperature variable clustering can be obtained by setting the maximum value of evaluation model as the objective. Finally, correlation coefficients between temperature variables and thermal error are calculated in order to find out optimum temperature variables for thermal error modeling. An experiment is conducted on a precise horizontal machining center. In experiment, three displacement sensors are used to measure spindle thermal error and twenty-nine temperature sensors are utilized to detect the machining center temperature. Experimental result shows that the new method of temperature variable optimization on optimal threshold successfully worked out a best threshold value interval and chose seven temperature variables from twenty-nine temperature measuring points. The model residual of z direction is within 3 μm. Obviously, the proposed new variable optimization method has simple computing process and good modeling accuracy, which is quite fit for thermal error compensation.

Spindle Thermal Error Optimization Modeling of a Five-axis Machine Tool

Aiming at the problem of low machining accu- racy and uncontrollable thermal errors of NC machine tools, spindle thermal error measurement, modeling and compensation of a two turntable five-axis machine tool are researched. Measurement experiment of heat sources and thermal errors are carried out, and GRA（grey relational analysis） method is introduced into the selection of tem- perature variables used for thermal error modeling. In order to analyze the influence of different heat sources on spindle thermal errors, an ANN （artificial neural network） model is presented, and ABC（artificial bee colony） algorithm is introduced to train the link weights of ANN, a new ABC- NN（Artificial bee colony-based neural network） modeling method is proposed and used in the prediction of spindle thermal errors. In order to test the prediction performance of ABC-NN model, an experiment system is developed, the prediction results of LSR （least squares regression）, ANN and ABC-NN are compared with the measurement results of spindle thermal errors. Experiment results show that the prediction accuracy of ABC-NN model is higher than LSR and ANN, and the residual error is smaller than 3 pm, the new modeling method is feasible. The proposed research provides instruction to compensate thermal errors and improve machining accuracy of NC machine tools.

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.

Bayesian networks modeling for thermal error of numerical control machine tools

The interaction between the heat source location, its intensity, thermal expansion coefficient, the machine system configuration and the running environment creates complex thermal behavior of a machine tool, and also makes thermal error prediction difficult. To address this issue, a novel prediction method for machine tool thermal error based on Bayesian networks (BNs) was presented. The method described causal relationships of factors inducing thermal deformation by graph theory and estimated the thermal error by Bayesian statistical techniques. Due to the effective combination of domain knowledge and sampled data, the BN method could adapt to the change of running state of machine, and obtain satisfactory prediction accuracy. Ex- periments on spindle thermal deformation were conducted to evaluate the modeling performance. Experimental results indicate that the BN method performs far better than the least squares (LS) analysis in terms of modeling estimation accuracy.

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.

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 fore- casting accuracy resulted from the volatility of tempera- ture-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 modelingindependent variable in the application of thermal error compensation of CNC machine tools.

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.

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%.

Thermal error modeling based on BiLSTM deep learning for CNC machine tool

The machining accuracy of computer numerical control machine tools has always been a focus of the manufacturing industry.Among all errors,thermal error affects the machining accuracy considerably.Because of the significant impact of Industry 4.0 on machine tools,existing thermal error modeling methods have encountered unprecedented challenges in terms of model complexity and capability of dealing with a large number of time series data.A thermal error modeling method is proposed based on bidirectional long short-term memory(BiLSTM)deep learning,which has good learning ability and a strong capability to handle a large group of dynamic data.A four-layer model framework that includes BiLSTM,a feedforward neural network,and the max pooling is constructed.An elaborately designed algorithm is proposed for better and faster model training.The window length of the input sequence is selected based on the phase space reconstruction of the time series.The model prediction accuracy and model robustness were verified experimentally by three validation tests in which thermal errors predicted by the proposed model were compensated for real workpiece cutting.The average depth variation of the workpiece was reduced from approximately 50μm to less than 2μm after compensation.The reduction in maximum depth variation was more than 85%.The proposed model was proved to be feasible and effective for improving machining accuracy significantly.

Key point selection in large-scale FBG temperature sensors for thermal error modeling of heavy-duty CNC machine tools

Thermal error is one of the main factors that influence the machining accuracy of computer numerical control(CNC)machine tools.It is usually reduced by thermal error compensation.Temperature field monitoring and key temperature measurement point(TMP)selection are the bases of thermal error modeling and compensation for CNC machine tools.Compared with small-and medium-sized CNC machine tools,heavy-duty CNC machine tools require the use of more temperature sensors to measure their temperature comprehensively because of their larger size and more complex heat sources.However,the presence of many TMPs counteracts the movement of CNC machine tools due to sensor cables,and too many temperature variables may adversely influence thermal error modeling.Novel temperature sensors based on fiber Bragg grating(FBG)are developed in this study.A total of 128 FBG temperature sensors that are connected in series through a thin optical fiber are mounted on a heavy-duty CNC machine tool to monitor its temperature field.Key TMPs are selected using these large-scale FBG temperature sensors by using the density-based spatial clustering of applications with noise algorithm to reduce the calculation workload and avoid problems in the coupling of TMPs for thermal error modeling.Back propagation neural network thermal error prediction models are established to verify the performance of the proposed TMP selection method.Results show that the number of TMPs is reduced from 128 to 5,and the developed model demonstrates good prediction effects and strong robustness under different working conditions of the heavy-duty CNC machine tool.

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.

Practical Calculation of Thermal Deformation and Manufacture Error in Surface Grinding

The paper submits a method to calculate thermal deformation and manufacture error in surface grinding. The author established a simplified temperature field model, and derived the thermal deformation of the ground workpiece. It is found that there exists not only a upwarp thermal deformation, but also a parallel expansion thermal deformation. A upwarp thermal deformation causes a concave shape error on the profile of the workpiece, and a parallel expansion thermal deformation causes a dimension error in height. The calculations of examples are given and compared with presented experiment data.

基于改进松鼠搜索算法优化神经网络的数控机床进给系统热误差预测

为探究数控机床进给系统中各因素对热误差的影响规律,建立精准的热误差预测模型。在进给速度为10 m/min、环境温度20℃的条件下进行进给系统热误差测量实验,获得进给系统关键点的温升及热误差。为提高预测精度,采用Tent混沌改进松鼠搜索算法,并利用改进的算法对神经网络进行优化,建立热误差预测模型。利用热误差测量实验获得的数据进行验证,结果表明改进前的神经网络预测误差为12.23%,改进后的模型预测误差为8.92%,精度有较大提升。利用预测模型针对不同进给速度下相同位置处热误差进行分析,结果表明,进给系统中关键测温点的温度和丝杠各点的热误差随着进给速度的增加而增加。因此提出的预测模型可实现进给系统热误差的准确预测,为误差补偿提供理论依据。

主轴热偏移特性检测与影响分析

主轴的热偏移误差直接影响数控机床的定位精度及加工质量。针对数控机床主轴温升及热偏移误差的测试问题,利用磁吸式温度传感器、激光测试仪对主轴温度变化与各方向热偏移误差进行测量,了解机床主轴实际工况,收集主轴温升数据及热平衡时间、主轴热偏移空间状态,分析主轴温升与热偏移误差随测试组数的变化关系。实验结果表明:主轴温升与测试组数的增加呈正比例关系,高转速最大温升12.65℃,明显高于低转速温升8.55℃;主轴各方向热偏移误差随温升而变大,其中高转速下误差变化更剧烈,高、低转速下的最大热偏移分别为35.37μm和15.26μm。本研究成果为以后探寻误差成因及优化补偿设计提供了数据支撑。

数控转台蜗轮转动元动作的热变形有限元分析

为研究数控转台传动系统元动作的热误差建模方法,首先介绍了元动作理论以及有限元数值模拟的温度场模型假设及边界条件,并分析了温度场仿真所需的摩擦机理及参数计算方法.采用ANSYS仿真分析从稳态和瞬态两个方面对转动元动作进行数值模拟,并给出其中关键元动作的温升曲线,分析了元动作瞬态、稳态及热-结构耦合温度场和变形场,得到元动作温度分布云图、温升量及热变形量.通过热变形理论计算和热变形有限元分析,得到考虑动作件热变形的传动系统元动作热误差模型.

数控机床旋转轴多自由度静/热误差同步测量与建模

针对现有的数控机床旋转轴误差测量与建模方法仅考虑多自由度静态几何误差或单自由度热误差单独作用的影响,未考虑几何误差和热误差耦合影响的问题,提出了一种基于球杆仪的数控机床旋转轴多自由度静/热误差同步测量与建模方法。首先基于齐次坐标变换建立球杆仪杆长变化模型,再基于该模型使用非齐次线性方程组建立静/热误差辨识模型;其次设计了适应多自由度静/热误差同步测量的球杆仪安装模式以缩短测量时间,减少热逸散对测量结果的影响;再次基于卷积长短期记忆神经网络(CNN-LSTM)建立旋转轴多自由度静/热误差预测模型;最后在数控蜗杆砂轮磨齿机的C轴上进行误差测量实验,对多种转速下的旋转轴多自由度误差进行快速辨识,并通过CNN-LSTM静/热误差预测模型对多自由度误差和球杆仪杆长变化进行预测,以验证所建模型的准确性。

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.

基于气动位移传感器的纵切车床热误差测量方法研究

针对传统纵切车床热误差测量方法时间长,导致测量过程中的瞬态热行为影响测量结果的问题,提出了一种基于气动位移传感器的纵切车床热误差测量方法。首先,将两个气动差动变压器(Linear Variable Displacement Transducer,LVDT)式位移传感器安装于刀具架上;其次,利用数控程序控制测量,可以实现刀具运动空间内X和Y方向热误差的测量,测量时间缩短至5 s;最后,进行热误差测量实验。实验表明:这种方法的测量结果与激光干涉仪测量结果平均偏差小于0.5μm,验证了这种方法的可行性与有效性。这种方法测量时间短、成本低,为纵切车床热误差补偿提供了依据。

纵切自动车床环境温度变化误差试验研究

为研究环境温度变化对纵切自动车床热误差的影响,本文以RTS20瑞士型纵切自动车床为试验载体,通过环境温度变化误差(ETVE)试验装置,开展了恒主轴转速(5000 rpm)变环境温度、变主轴转速(0~8000 rpm)变环境温度的热误差试验,对环境温度对热误差的影响进行了研究分析。结果表明:在相同主轴转速下,环境温度变化影响着车床结构各部位的温升,进而影响热误差;在随机主轴转速下,主轴轴承等温度测点的温升受转速变化的影响,热误差的增长与关键测点温度具有相同变化周期,但不具有相同变化趋势,即温度快速升高时热误差并不一定也正向增长;空调开启后各测点温度有明显降低,最高降幅可达3℃,热误差随之也有明显降低趋势,最高降幅达6μm。

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。