通过热量误差补偿来改善数控机床的外文翻译精确度数控机床类中英文翻译@外文文献翻译

外文原文 IMPROVING ACCURACY OF CNC MACHINE TOOLS THROUGH COMPENSATION FOR THERMAL ERRORS Abstract： 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. Key words : CNC machine tool Thermal error Compensation 0 INTRODUCTION Improvement of machine tool accuracy is essential to quality cont rol in manufacturing processes. Thermally induced errors have been recognized as the largest cont ributor to overall machine inaccuracy and are probably the most formidable obstacle to obtaining higher level of machine accuracy. Thermal errors of machine tools can be reduced by the st ructural improvement of the machine tool it self through design and manufacturing technology. However , there are many physical limitations to accuracy which can not be overcome solely by production and design techniques. So error compensation technology is necessary. In the past several years , significant effort s have been devoted to the study. Because thermal errors vary with time during machining ,most previous works have concent rated on real-time compensation. The typical approach is to measure the thermal errors and temperature of several representative point s on the machine tools simultaneously in many experiment s , then build an empirical model which correlates thermal errors to the temperature statues by multi-variant regression analysis or artificial neural network.During machining , the errors are predicted on-line according to the pre-established model and corrected by the CNC cont roller in real-time by giving additional signals to the feed-drive servo loop.However , very few practical cases of real-time compensation have been reported to be applied to commercial machine tools today. Some difficulties hinder it s widespread application. First , it is tedious to measure thermal errors and temperature of many point s on the machine tools. Second ,the wires of temperature sensors influence the operating of the machine more or less. Third , thereal-time error compensation capability is not available on most machine tools. In order to improve the accuracy of production-class CNC machine tools , a novel method is proposed. Although a number of heat sources cont ribute to the thermal errors , the f riction of spindle bearings is regarded as the main heat source. The thermal errors are measureed by 1-D ball array and a spindle-mounted probe. An auto regressive model based on spindle rotation speed is then developed to describe the time-variant thermal error. Using this model , thermal errors can be predicted as soon as the workpiece NC machining program is made. By modifying the program , the thermal errors are compensated before machining. The effort and cost of compensation are greatly reduced. This research is carried on a JCS2018 vertical machining center. 1 EXPERIMENTAL WORK For compensation purpose , the principal interest is not the deformation of each machine component , but the displacement of the tool with respect to the workpiece. In the vertical machining center under investigation , the thermal errors are the combination of the expansion of spindle , the distortion of the spindle housing , the expansion of three axes and the distortion of the column. Due to the dimensional elongation of leadscrew and bending of the column , the thermal errors are not only time-variant in the time span but also spatial-variant over the entire machine working space. In order to measure the thermal errors quickly , a simple protable gauge , i. e. , 1-D ball array , is utilized. 1-D ball array is a rigid bar with a series of balls fixed on it with equal space. The balls have the same diameter and small sphericity errors. The ball array is used as a reference for thermal error measurement . A lot of pre-experiment s show that the thermal errors in z-axis are far larger than those in x-axis and y-axis , therefore major attention is drawn on the thermal errors in z-axis. Thermal errors in the other two axes can be obtained in the same way. The measuring process is shown in Fig.1. A probe is mounted on the spindle housing and 1-D ball array is mounted on the working table. Initially , the coordinates of the balls are measured under cold condition. Then the spindle is run at a testing condition over a period of time to change the machine thermal status. The coordinates of the balls are measured periodically. The thermal drift s of the tool are obtained by subt racting the ball coordinates under the new thermal status f rom the reference coordinates under initial condition. Because it takes only about 1 min to finish one measurement , the thermal drifts of the machine under different z coordinates can be evaluated quickly and easily. According to the rate of change , the thermal errors and the rotation speed are sampled by every 10 min. Since only the drift s of coordinates deviated from the cold condition but not the absolute dimensions of the gauge are concerned , accuracy and precise inst rument such as a laser interferometer is not required. There are only four measurement point s z 1 ,z 2 , z 3 , z 4 to cover the z-axis working range whose coordinates are - 50 , - 150 , - 250 , - 350 respectively. Thermal errors at other coordinates can be obtained by an interpolating function. Previous experiment s show that the thermally induced displacement between the spindle housing and the working table is the same with that between the spindle and table. So the thermal errors z measured reflect those in real cutting condition with negligible error. In order to obtain a thorough impression of the thermal behavior of the machine tool and identify the error model accurately , a measurement strategy is developed. Various loads of the spindle speed are applied. They are divided into three categories as the following : (1) The constant speed ； (2) The speed spect rum ； (3) The speed simulating real cutting condition. The effect of the heat generated by the cutting process is not taken into account here. However , the influence of the cutting process on the thermal behaviour of the total machine structure is regarded to be negligible in finishing process. In this machine , the most significant heat sources are located in the z-axis. Thermal errors in z direction on different x and y coordinates are approximately the same. It implies that the positions of x-carriage and y-carriage have no strong influence on the z-axis thermal errors. Fig.1（ L） Thermal error measurement 1.Spindle mounted probe 2.1-D ball array Fig.2 （ R） Thermal errors at different z coordinates 1. z = - 50 2. z = - 150 3. z = - 250 4. z = - 350 Fig.2 plot s the time-history of thermal drift z at different z coordinates under a test . It shows that the resultant thermal drift s are obvious position-dependent . The thermal drift s at z 1 ,z 2 , z 3 , z 4 are coincident initially but separate gradually as time passes and temperature increases. The reason is that , initially most of thermal drift s result f rom the position-independent thermal growth of the spindle housing which would rise fast and go to thermal-equilibrium quickly compared to other machine component s with longer thermal-time-constant s. However , as time passes , those position-dependent thermal errors such as the lead screw and the column cont ribute to the resultant thermal drift s of the tool more and more. As a result , the thermal drifts at different z coordinates have different magnitude and thermal characteristics. However , the thermal errors at different coodinates vary with z coordinate continuously. 2 AR MODEL FOR THERMAL ERROR Precise prediction of thermal errors is an important step for accurate error compensation. Since the knowledge of the machine structure , the heat source and the boundary condition are insufficient , a precise quantitative prediction based on theoretical heat transfer analysis is quite difficult . On the other hand , empirical-based error models using regression analysis and neural networks have been demonst rated to predict thermal errors with satisfactory accuracy in much application. Thermal errors are caused by various heat sources. Only the influence of the heat caused by the fiction of spindle which is the most significant heat source is considered. The influence of external heat source on machining accuracy can be diminished by environment temperature control. From the obtained data , it is found that thermal errors vary continuously with time. The value of error at one moment is influenced by that of the previous moment and the rotation speed of spindle. So a model representing the behavior of the thermal errors as written is the form where z ( t) Thermal error at time t k , m Order of the model ai , bi Coefficient of the model n ( t - i) Spindle rotation speed at time t - i The order k and m are determined by the final prediction-error criterion. The coefficients ai and bi are estimated by artificial neural network technique. A neural network is a multiple nonlinear regression equation in which the coefficient s are called weight s and are t rained with an iterative technique called back propagation. It is less sensitive than other modeling technique to individual input failure due to thresholding of the signals by the sigmoid functions at each node. The neural network for this problem is shown in Fig.3. ( k = 1 , m = 0) . The number of hidded nodes is determined by a trial-and error procedure. Using the data obtained (thermal errors and correspondence speed) , four models for the errors at z 1 , z 2 , z 3 and z 4 are established. Thermal errors at positions other than z 1 , z 2 , z 3 , z 4 are calculated by an interpolating function. So the errors at any z coordinates can be obtained. In order to verify the prediction accuracy of the model , a number of new operation conditions are used. Fig14 shows an example of predicted result on a new condition. It shows that the auto regressive model based on speed can descibe thermal errors well in a relative stable environment . Fig.3 A neural network for thermal errors Fig.4 Thermal error predicting 1.Measuring results 2Predicting results 3 PRE-COMPENSATION FOR THERMAL ERRORS The principle of pre-compensation for thermal errors is shown in Fig.5. The spindle rotation speed and the z coordinates are known as soon as the workpiece NC machining program is made. By , for example , every 10 min , the thermal errors z are calculated by the model. Then the program is corrected by adding the calculated z to the original z . So the thermal errors are compensated before machining. The effectiveness of the error compensation is verified by many cutting test s. Several surfaces are milled under cold start and after 1 h run with varying speeds. As shown in Fig.6 , the depth difference of the milled surface is used to evaluate the compensation result of the thermal errors in z direction. It shows that the difference is reduced from 7m to 2 m. Fig.5 Compensation for thermal errors by revising machining program Fig.6 The effectiveness of compensation 4 CONCLUSIONS A novel method for improving the accuracy of CNC machine tools is discussed. The core of the study is an error model based on spindle rotation speed but not on temperature like conventional approach. By revising the NC workpiece machining program , the thermal errors can be compensated before machining but not in real-time. By using the method , the accuracy of machine tools can be increased economically. References 1 Chen J S , Chiou G. Quick testing and modeling of thermally-induced errors of CNC machine tools. International Journal of Machine Tools and Manufacture , 1995 , 35(7) 1 063 1 074 2 Chen J S. Computer-aided accuracy enhancement for multi-axis CNC machine tool. International Journal of Machine Tools and Manufacture , 1995 , 35(4) 593 605 3 Donmez M A. A general methodology for machine tool accuracy enhancement by error compensation. Precision Engineering , 1986 , 8 (4) 187 196 4 Lo C H. An application of real-time error compensation on a turning center. International Journal of Machine Tools and Manufacture , 1995 , 35(12) 1 6691 682. 5 Yang S. The Improvement of thermal error modeling and compensation on machine tools by CMAC neural network. International Journal of Machine Tools and Manufacture , 1995 , 36(4) 527 537 6 李书和 1 数控机床误差补偿的研究 博士学位论文 1 天津天津大学 ,19961 译文： 通过热量误差补偿来改善数控机床的精确度 摘 要： 通过热量误差补偿来改变数控机床的精度是一种可行的方法。热量误差的获得是通过 1-D滚珠排列和建立在锭子转速基础上的自动退刀的表征。通过改变工件的数控程序，热量误差在机加工以前可以被补偿。试验表明直立的加工中心的实际补偿是可行的。 关键词： 数控加工中心 , 热量误差 ,补偿 0.引言： 数控机床精确度的改善是生产过程中质量控制的根本。热量误差已经被作为机器精确度失衡的最大诱因，而且可能也是机器获取更高精确度的最大障碍。数控机床的热量误差可通过机床本身的结构设计和生产技术的改善而降低。尽管如此，还是有许多物 理性限制因素使得精确度不能通过生产和设计技术而单独克服。因此，误差补偿技术是很必要的。在过去的几年里，对此技术的研究已经获得重大成果。由于热量误差在加工时随时间而变化，许多前人的工作都集中在实际时间的的补偿比率上。典型的方法是对机床几个有代表性的点进行热量误差和温度的同步试验，然后建立一个与热量误差和温度的试验模型对多种变化进行回归分析或是人工网络分析。 在加工期间，误差是根据之前建立的模型进行预测并通过在实际过程中用额外的信号和自由回路进行改正的。但是，目前只有很少被报道的实际过程补偿案例适用于商业机床。 首先，对机床的多个点进行热量误差和温度的测量是不可取的。其次，温度传感器的线会或多或少影响机器的运转。第三，实际操作中的误差补偿功能在许多的机器上是不可用的。 为了改善数控机床生产的精确度，有个方法是值得尝试的。尽管许多的热源都能引起热量误差，但是环形轴承的摩擦被认为是最主要的热源。热量误差是由 1-D 滚珠排列 来衡量的。一个自动回归模型是以 锭子转速然后被发展到描述那时的热量错误为基础的。利用这个模型，热量误差能够在机械加工程序制造的时候被预测出来。通过对程序的修订，热量误差能够在加工之前得到补偿。那么补偿的代 价就大大的减轻了。 1.试验工作 为了达到补偿目的，重要的部分不是每个机器的零部件，而是工件的位移。在调查的线性机械加工中心中，热量误差是由锭子膨胀、锭子固件变形和三个轴空间的变形一起引起的。由于导杆的伸长和栏的弯曲，热量误差并不只是在时间上的改变，而且还是机械加工在空间上的变化。 为了能够快速的测量热量误差，一些简单的量规是可以使用的，例如：滚珠排列。滚珠排列是把一系列的滚珠按相等的间隔固定在顶梁上。由于滚珠的直径相等，球状的误差比较小，因此，滚珠排列被用于热量误差测量的一个参考。大量的之前试验数据表明 在光轴上的热量误差远远高于在横轴和纵轴。所以，热量误差主要关注在光轴上。同理，也可以用相同的办法得到其他两个轴上的热量误差数据。测量的过程如图 1所示：刚开始，滚珠的坐标是处在低温状态的，然后锭子在试验状态下改变机器的热量。滚珠温度的测量是周期性的。热量的转移是通过用最初的参考坐标减去在新的热量状态下滚珠坐标来实现的。由于这种测量只需要一分钟，机器在不同坐标下的热量转移能够更快更容易的被显现出来。根据转动速率的变化，热量误差和转速是每十分钟就是一个循环。坐标的唯一偏离是在低温状态下完成的，而不是在所关注的独立 的量规尺寸下。象激光干涉仪这样的精确度和准确度装置并不做要求。只有四个测量点 z1， z2， z3， z4来覆盖坐标为 -50， -150， -250， -350 的z 坐标的工作范围。在其他的坐标中热量误差可以通过一个插值函数来获得。 上述的试验说明了在锭子位置和工作台之间的派生位移与锭子和台之间是一致的。因此热量误差 z 的测量反映了在真正的切割条件下误差是可以忽略的。 为了能够获得机床热量行为的全面理解以及正确的判断误差模型，形成了一种测量方法。锭子转速的多种加载方式是可用的。他们被分为如下三类： 1，常规转速， 2，转速范围 ， 3，真正切割状态下的同步转速。此处，由切割过程而引起的热量作用没有被考虑进来。不过，切割过程对整个机床机构的热量的影响在最终的过程中是可以忽略的。在这种机床中，最大的热源来自于 z 轴。热量误差在 z 方向和不同的 x和 y 坐标方向大约是相同的。也就是说 x轴和 y轴的位置对 z 轴的热量误差没有重大影响。 1.图 1（左） 热量误差测量 锭子传感器 2.1-D 滚珠排列 图 2（右） 在不同 z坐标中的热量误差 1. z = - 50 2. z = - 150 3. z = - 250 4. z = - 350 图 2 在测试中不同 z 坐标中热量转移时间过程图的绘制 上图表明合成的热量转移明显是由所在决定的。在 z1， z2， z3， z4 点上的热量转移刚开始是一样的，然后随着时间的流逝和温度的增加而逐渐分离。原因在于最初大量的热量转移是由于锭子位置的增长造成的，和其他的耐热时间较长的机床部件相比，这个位置能更快的达到热量平衡。然而，随着时间的过去，那些象 导螺杆和栏这样由位置决定热量误差的部件越来越多的引起合成热量的转移。结果，在不同的z 坐标中热量的转移具有不同的大小和热量特性。但是，不同坐标中的热量转移是随 z 坐标不断改变的。 2.热量误差的回归模型 热量误差的准确预测是精确误差补偿的重要环节。由于对机床结构的认识和热源以及界限条件的不充分，根据热量传递分析得出精确的数量测量是非常困难的。另外，在众多的实用中，利用以经验为基础的误差模型进行回归分析和网络分析来准确预测热量误差是不可能的。热量误差是由多种热源引起的，而只有锭子引起的热量被认为是最重要的