基于复杂曲面的多轴数控加工非线性误差的控制研究

通过分析非线性误差及其对多轴数控加工的影响,提出了基于复杂曲面的多轴数控加工中非线性误差的控制方法,以期为提高多轴数控机床的加工精度贡献力量。     

数控机床RTCP精度的分析

RTCP精度是五轴联动数控机床的重要精度指标.本文是基于西门子840D数控系统,结合航空大型结构件加工企业高档五轴数控加工中心的实际情况,分析并总结出一套数控机床RTCP精度的检验与补偿的方法.     

数控机床反向误差测定评价及补偿

建立了反向误差补偿的数学模型,提出了数控机床的反向误差的软件补偿方法.对补偿前后的反向误差进行了精度评价,结果表明,经过反向误差的软件补偿,数控机床位置误差降低,实现了机床精度的软升级.     

基于PLC技术的机电一体化数控机床运行控制研究

针对传统机电数控机床运行控制效果不佳，导致加工精度较低的问题，提出基于误差补偿技术和ARM平台，构建一个基于PLC技术的综合误差实时补偿系统。首先，分别对数控机床温度和机床参数进行采集和存储；然后通过训练的BP神经网络进行误差计算，以建立机床-几何-热切削力的综合误差模型；最后利用误差补偿方法计算当前时刻误差补偿值，并发送至机床PLC实现误差实时补偿。实验表明，通过本系统可直观监测机床加工状态，实现误差补偿参数设置。QLM27100型机床测试中，曲面1的最大误差和平均误差分别为84.6μm和39.74μm;曲面2的最大误差和平均误差分别为52.81μm和13.17μm,对比于曲面1,曲面2的误差更小，测量精度更高。由此可知，本系统在机床加工中可减小误差，提升加工精度，实现机电一体化和智能化控制。     

回转轴运动精度的干涉测量与误差补偿分析

采用激光干涉仪检测混联数控机床回转轴的运动精度,达到对其综合评价的目的,为机床的运动精度误差补偿做准备.对雷尼绍激光干涉仪的结构和角度测量原理进行了详细探讨,分析了干涉仪在回转轴运动精度测量中的主要影响因素和误差诱因;作出了角度测量中正弦近似误差特性曲线,并提出了该测量误差的数学模型;讨论了激光波长补偿的机理,提出了一种通用、简便易行的回转轴角位移精度检测方法.     

闭环伺服系统的动态性能分析

在数控机床中 ,伺服系统是数控装置和机床的中间联接环节 ,是数控系统的重要组成部分。伺服系统接受来自伺服控制器的进给脉冲 ,经变换和放大后转化为机床工作台的位移 ,使工作台跟随指令脉冲移动。文章研究了闭环伺服系统的动态性能分析。该研究结果为伺服数控加工系统的快速响应及其加工精度提供理论基础。     

基于西门子PLC的智能工业机电一体化机床控制研究

针对机床加工精度低和稳定性差的问题，提出以数控机床为研究对象，设计一种基于PLC的智能工业机电一体化机床控制系统。首先构建热误差建模算法，然后通过PLC实现热误差的补偿控制调节，可提高机床的加工精度和稳定性。具体实现为，由状态信息采集模块实现机床温度的测量；再在上位机数据处理模块中，通过建立机床温度与热误差同步测量系统，实现对热误差值的预测；再将预测值输入PLC控制模块，由PLC控制程序完成热误差预测值的补偿；并且对SIEMENS系统温度补偿功能的热误差补偿方法进行了研究。实验结果证明：在热误差补偿功能开启后，热误差最大值降低了78.9%。由此可知，本研究构建的机床控制系统可有效提高机床的加工精度以及稳定性，在实际应用中具有可行性。     

数控加工刀具变形误差补偿技术研究

在数控铣削加工的研究中,刀具变形引起的加工误差对工件的加工精度影响较大.研究了一种考虑切屑厚度影响的切削力模型,建立了由切削力引起的刀具变形加工误差的分析模型.为了提高加工精度,提出了一种线性迭代误差补偿算法,方法主要对由切削力引起的刀具变形产生的切削误差进行循环迭代补偿.通过应用数控加工中心,方案进行了验证,对补偿后...     

基于轮廓最优圆逼近方法的轮廓误差控制

设计并实现了基于轮廓最优圆逼近方法的轮廓误差估计模型,该模型克服了常规方法对于任意加工曲线,廓误差计算过程复杂、计算量大、难以应用到实时任务的缺点,通过实时读取数控机床的位置反馈值和插补器的指令数据估算出轮廓误差值,结合双模糊变论域自适应控制算法,应用于两轴数控系统并实时补偿各单轴控制器,实现对复杂加工过程轮廓误差实时估算并补偿。在实验数控机床上加工两种典型轮廓,对比实验表明,所提出的基于轮廓最优圆逼近方法的双模糊变论域轮廓误差控制方法能有效减小加工轮廓误差,有更高的轮廓精度。     

基于UG的五轴数控机床加工仿真

目前多数五轴数控机床仿真系统,一般只提供二维的动画仿真,而且仿真系统的几何造型功能十分有限,零件和机床模型需要在其他CAD软件中进行建模,然后导人数控仿真系统。由于文件格式的转化,零件的CAD模型将会产生误差,降低了仿真精度。该文利用UGCAD／CAM软件造型功能建立五轴数控机床和零件模型,读取数控代码对机床各部件进行三维运动仿真,并对加工过程中机床运动部件之间的干涉及工件过切进行检查,建立干涉实体,为刀具轨迹的修改提供依据,同时免除了文件格式的转化产生的误差。     

Error compensation and accuracy improvements in 5-axis machine tools using the global offset method

To enhance the machine tool accuracy, the Global offset method is developed for compensating the five-axis machine tool errors based on the measurement results of one or more identical machined parts. The machined features of a part are measured in a CMM and evaluated by a compensation processor, based on which the Global offset parameters, representing the machine tool errors, are estimated. The methodology is capable of compensating the overall effect of all position-dependent and position-independent systematic errors which contribute to particular workpiece accuracy. The developed technique and software are based on the Global offset method which interprets the computed deviations between the measured and nominal dimensions of the part through the analysis, synthesis and modeling of a fixture and rotary tables errors. The proposed model-based error compensation method is simple enough to be implemented in five-axis CNC machine tools. Production results exhibit effective compensation and remarkable improvement in the workpiece accuracy of the five-axis machine tools.     

基于LabVIEW数控机床几何误差补偿的研究方法

介绍一种由软件、硬件控制的数控机床几何误差补偿。误差补偿的原理主要是运用多体系统运动学理论建立机床几何误差模型,用硬件控制固化在程序存储器内的误差补偿程序完成补偿任务,并通过RS-232C实现数控机床与Windows平台通信与数据交换。     

数控机床的螺距误差检测及补偿

本文介绍了数控机床的螺距误差补偿原理和基于激光干涉仪的螺距误差测量系统,并且讨论了在螺距误差测量中所出现的问题和解决方案,最后介绍了螺距误差补偿的适用范围。     

数控机床工作台抖动的原因及分析

针对目前维修过程中经常遇到的有关机床工作台抖动的现象,进一步分析阐述了引起数控机床工作台抖动的原因及处理方法,有效避免在加工过程中由于工作台抖动而引起零件质量问题。     

Adaptive learning control for thermal error compensation of 5-axis machine tools

The research presented in this paper shows an adaptive approach for long-term thermal error compensation of 5-axis machine tools (MT). A system of differential equations is used to compute the model based compensation values. The model can predict thermal displacements of the tool center point (TCP) based on changes in the environmental temperature, load-dependent changes and boundary condition changes and states, like machining with or without cutting fluid. The model based compensation of the rotary axis of a 5-axis MT is then extended by on-machine measurements. The information gained by the process-intermittent probing is used to adaptively update the model parameters, so that the model learns how to predict thermal position and orientation errors and to maintain a small residual error of the thermally induced errors of the rotary axis over a long time. This approach not only increases the MT accuracy but also reduces the amount of time spent on preproduction model parameter identification. Additionally an algorithm has been developed to dynamically adjust the length of the on-machine measurement intervals to maintain a high productivity and a constant deviation of the machined parts.Experimental results confirm that the adaptive learning control (ALC) for thermal errors shows a desirable long-term prediction accuracy.     

The application of ANFIS prediction models for thermal error compensation on CNC machine tools

Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering method (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible temperature sensors on the thermal response of the machine structure. All the influence weightings of the thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the groups then being further reduced by correlation analysis.A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent testing data sets. The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual value of the proposed model is smaller than ±4 μm. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system.     

基于AT89C55单片机的数控机床远程测控仪的研制

研制了一种基于AT89C55单片机的数控机床远程测控仪,主要介绍了该测试仪的硬件和软件设计,详述了数控机床远程测控的基本原理,并在数控机床远程控制平台上进行了直线插补和圆弧插补等插补精度的实时远程测试和误差分析。该测试仪已在多家单位生产实践中应用,测试效果良好。     

数控车床定位精度的激光检测法

数控机床的定位精度是反映数控机床控制特性和加工质量的重要指标,直接决定着零件的加工精度。如果机床的定位精度超差,则会导致加工出的零件不合格,这也直接影响了数控机床性能与功能的发挥。为了快速、准确检测出机床的定位误差并进行误差补偿,阐述了利用双频激光干涉仪进行数控机床定位精度检测的方法。     

A prediction and compensation method of robot tracking error considering pose-dependent load decomposition

Industrial robots are widely used because of their high flexibility and low cost compared with CNC machine tools, but the low tracking accuracy limits their application in the field of high-precision manufacturing. To improve the tracking accuracy and solve the complex modeling problems, a prediction and compensation method of robot tracking error is proposed based on temporal convolutional network (TCN), where the pose-dependent effect of load on joint tracking error is considered. The terminal load is decomposed to joint load by using Jacobian matrix and then used as the pose-dependent information of the data-based model. A prediction model based on TCN is used to predict the tracking error of joints. Finally, a pre-compensation method is adopted to improve the joint tracking accuracy based on the predicted errors. Experimental results show that the model presents good prediction and compensation accuracy. The mean absolute tracking errors are increased by more than 80% in the test path. This method can effectively compensate the tracking errors of the robot joints and therefore greatly improve the tracking accuracy of the tool center point and tool orientation in the Cartesian coordinate system.     

Dynamic reliability of thermally deduced positioning precision of ball screw systems based on random moving difference method

The positioning precision of a half-closed-loop feed system of machine tools is usually affected by the frictional heat of the system. The axial thermal errors of the working table exceeding the axial allowed tolerance is a type of failure mode of the ball screw system in positioning precision. At present, thermal error prediction and compensation methods have poor accuracy retention because they do not consider the randomness of the parameters. In order to solve the problem, we present a new random moving difference method of studying a tree-like heat transfer with thermally excited moving branches. Furthermore, based on the random moving difference method, a dynamic reliability prediction model (DRPM) of thermally deduced positioning precision is proposed, which was used in the study for predicting the dynamic variation performance of the heat boundaries in the ball screw feed drive system and the dynamic reliability of positioning precision of the system. Finally, the prediction performance of the presented method is verified under a CNC lathe. Our method provides the theoretical and practical foundation for a real-time monitoring positioning precision reliability index while describing the positioning precision accurately because it effectively takes into account the random parameters.