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.

     

Response surface modeling and optimization of single axis automatic application of automotive polyurethane coatings on plastic components

This paper proposes a novel modeling methodology for machine tool thermal error. This method combines the advantages of both grey model and artificial neural network (ANN) in terms of data processing. To enhance the robustness and the prediction accuracy, two kinds of grey neural network, namely serial grey neural network (SGNN) and parallel grey neural network (PGNN), are proposed to predict the thermal error. Experiments on the axial directional spindle deformation on a five-axis machining center are conducted to build and validate the proposed models. The results show that both SGNN and PGNN perform better than the traditional grey model and ANN in terms of prediction accuracy and robustness. So the new models are more suitable for complex working conditions in industrial applications.     

基于神经模糊控制理论的数控机床热误差建模

将基于神经模糊控制理论的建模方法--模糊神经网络建模法应用到数控机床热误差建模当中,讨论了热误差模糊神经网络的结构及建模原理;对大型数控龙门导轨磨床主轴箱系统进行建模试验,采用非接触式红外温度测量仪和千分表分别测量主轴箱系统温度值与主轴热误差,得到两组独立的试验数据,一组用来建立主轴箱系统热误差模糊神经网络预报模型,另一组用来对模型进行验证。试验结果表明,模糊神经网络模型预测精度高,泛化能力强;将模糊神经网络建模方法与径向基函数神经网络建模方法进行综合对比,分析结果表明,模糊神经网络建模方法具有更好的建模效率、建模鲁棒性及预测性能。     

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。     

变工况下基于迁移学习融合域内对齐的机床主轴热误差模型

热误差建模和补偿是提高机床加工精度的重要手段。 将得到的热误差模型应用到类似或相近任务中,对减少模型构建 和数据收集的成本具有重要意义。 本文提出了一种简易迁移学习(EasyTL)融合域内对齐的主轴热误差建模方法,以实现不同 工况下误差模型的迁移复用。 建立基于域内对齐和距离矩阵全组合择优的热误差迁移模型参数选取方法,获得最优组合。 进 一步分析不同类型的域内对齐和距离矩阵各自对模型迁移性能的影响。 最后,将迁移模型与 kNN 典型机器学习模型和卷积神 经网络深度模型进行比较验证,分别预测不同工况下主轴 Z 向和 Y 向的热误差。 此外,根据预测的主轴热误差进行工件补偿 加工实验。 该方法为热误差建模及补偿提供了一种新思路。     

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。     

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.     

Theory analysis and system identification methods on thermal dynamics characteristics of ballscrews

Empirical model of machine tools on thermal error has been widely researched, which can compensate for thermal error to some extent but not suitable for thermal dynamic errors produced by dynamic heat sources. The thermoelastic phenomenon of unidimensional heat transfer of ballscrews influenced by changeable heat sources is analyzed based on the theory of heat transfer. Two methods for system identification (the least square system identification and BP artificial neural network (ANN) system identification) are put forward to establish a dynamic characteristic model of thermal deformation of ballscrews. The model of thermal error of the X axis in a feed system of DM4600 vertical miller is established with a fine identification effect. Comparing the results of the two identification methods, the BP ANN system identification is more precise than the least square system identification.     

Theory analysis and system identification methods on thermal dynamics characteristics of ballscrews

Empirical model of machine tools on thermal error has been widely researched, which can compensate for thermal error to some extent but not suitable for thermal dynamic errors produced by dynamic heat sources. The thermoelastic phenomenon of unidimensional heat transfer of ballscrews influenced by changeable heat sources is analyzed based on the theory of heat transfer. Two methods for system identification (the least square system identification and BP artificial neural network (ANN) system identification) are put forward to establish a dynamic characteristic model of thermal deformation of ballscrews. The model of thermal error of the X axis in a feed system of DM4600 vertical miller is established with a fine identification effect. Comparing the results of the two identification methods, the BP ANN system identification is more precise than the least square system identification.     

Modified Elman network for thermal deformation compensation modeling in machine tools

Thermal deformation is one of the most significant causes of machining errors in machine tools. One effective method is to build a compensation system to offset the thermal errors. Therefore, an accurate model is the key part of the compensation system. This study proposed a modified Elman network (EN) to improve the prediction accuracy of the compensation model in machine tools. And the improved EN can be regarded as a feed-forward neural network with feedback from hidden layer and output layer as an additional set of inputs. The structure of this network determines its dynamic characteristic with memory function. On the other hand, thermal deformation of the spindle contributes the largest part of total thermal errors in precision machining. Then a precise finite element model of machine tool spindle was established. And a new method for calculating the heat transfer convection coefficient on the surface of the spindle was proposed in this paper. The improved EN was used to map the nonlinear relationship between temperature field and thermal errors of the spindle. At last, a verification experiment was implemented on a CNC center and some satisfying results were achieved.     

Thermal error modelling for real-time error compensation

A modelling strategy for the prediction of both the scalar and the position-dependent thermal error components is presented. Two types of empirical modelling method based on the multiple regression analysis (MRA) and the artificial neural network (ANN) have been proposed for the real-time prediction of thermal errors with multiple temperature measurements. Both approaches have a systematic and computerised algorithm to search automatically for the nonlinear and interaction terms between different temperature variables. The experimental results on a machining centre show that both the MRA and the ANN can accurately predict the time-variant thermal error components under different spindle speeds and temperature fields. The accuracy of a horizontal machining centre can be improved through experiment by a factor of ten and the errors of a cut aluminium workpiece owing to thermal distortion have been reduced from 92.4 µm to 7.2 µm in the lateral direction. The depth difference due to the spindle thermal growth has been reduced from 196 µm to 8 µm.     

基于信息粒化支持向量机的主轴热误差综合预测模型

主轴热变形是影响数控机床加工精度的主要因素。为提高主轴热误差的预测精度,提出了基于信息粒化支持向量机(SVM)的主轴热误差综合预测模型。使用信息粒化方法对采样温度数据与主轴热误差数据进行预处理,分别建立基于SVM的主轴热误差的回归预测模型和时间序列模型,通过计算两个模型权重系数,最终建立主轴热误差综合预测模型。以2MZK7150五轴数控可转位刀片工具磨床为研究对象,实验表明,较之于单一模型该模型具有良好的泛化能力和较高建模精度。     

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.     

采用改进灰色神经网络的铣床热误差补偿研究

数控铣床在铣削零件过程中,主轴会受到温度变化影响而发生热变形,导致铣削零件误差较大,从而降低产品精度。对此,采用一阶线性微分方程推导GM(1,1)模型,创建灰色预测模型。将神经网络模型与灰色预测模型进行组合,建立灰色神经网络预测模型。引用粒子群算法,在粒子群算法中增加变异操作和修改惯性权重系数,给出改进粒子群算法优化灰色神经网络预测模型的具体操作步骤。采用实验测试铣床铣削过程中所产生的热误差,并与预测模型进行比较。结果显示:在铣床主轴X、Y、Z轴三个方向上,灰色神经网络预测模型对铣床主轴补偿后,得到的残差较大;而改进灰色神经网络预测模型对铣床主轴补偿后,得到的残差相对较小。采用改进粒子群算法优化灰色神经网络预测模型,能够提高铣床主轴铣削精度。     

IMPROVING ACCURACY OF CNC MACHINE TOOLS THROUGH COMPENSATION FOR THERMAL ERRORS

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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.     

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。     

基于实时反馈的机床热误差在线补偿模型

为建立一种能够适应机床不同工况且具有准确预测能力的热误差补偿模型,提出一种基于限定记忆递推最小二乘法辨识热误差模型参数的机床热误差预测建模方法。该方法随着机床工作状况的改变,根据实时反馈的温度和热误差数据,采用递推方法对模型参数进行即时修正,使热误差模型能够及时跟踪机床系统的热特性变化,实现以较高的预测精度对机床热误差进行补偿。通过数控车床主轴轴向热误差辨识建模及补偿实验可以看出,限定记忆递推最小二乘法比一步最小二乘法辨识精度有较大提高,最大残差值减小了52.3%,标准差减小了67%。实验结果表明,利用该方法进行机床热误差模型参数辨识具有较高的预测精度和鲁棒性,有效可行。       

Thermal influence of the Couette flow in a hydrostatic spindle on the machining precision

Hydrostatic spindles are increasingly used in precision machine tools. Thermal error is the key factor affecting the machining accuracy of the spindle, and research has focused on spindle thermal errors through examination of the influence of the temperature distribution, thermal deformation and spindle mode. However, seldom has any research investigated the thermal effects of the associated Couette flow. To study the heat transfer mechanism in spindle systems, the criterion of the heat transfer direction according to the temperature distribution of the Couette flow at different temperatures is deduced. The method is able to deal accurately with the significant phenomena occurring at every place where thermal energy flowed in such a spindle system. The variation of the motion error induced by thermal effects on a machine work-table during machining is predicated by establishing the thermo-mechanical error model of the hydrostatic spindle for a high precision machine tool. The flow state and thermal behavior of a hydrostatic spindle is analyzed with the evaluated heat power and the coefficients of the convective heat transfer over outer surface of the spindle are calculated, and the thermal influence on the oil film stiffness is evaluated. Thermal drift of the spindle nose is measured with an inductance micrometer, the thermal deformation data 1.35 μm after running for 4 h is consistent with the value predicted by the finite element analysis's simulated result 1.28 μm, and this demonstrates that the simulation method is feasible. The thermal effects on the processing accuracy from the flow characteristics of the fluid inside the spindle are analyzed for the first time.     

加工中心热误差补偿的研究

热误差是加工中心的最大误差源。通过对机床热特性的实验和分析，利用神经网络模型对热误差进行了补偿，取得比较好的效果。