通過熱量誤差補償來改善數(shù)控機床的外文翻譯精確度數(shù)控機床類中英文翻譯@外文文獻翻譯

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 李書和 1 數(shù)控機床誤差補償?shù)难芯?博士學位論文 1 天津天津大學 ,19961 譯文： 通過熱量誤差補償來改善數(shù)控機床的精確度 摘 要： 通過熱量誤差補償來改變數(shù)控機床的精度是一種可行的方法。熱量誤差的獲得是通過 1-D滾珠排列和建立在錠子轉(zhuǎn)速基礎(chǔ)上的自動退刀的表征。通過改變工件的數(shù)控程序，熱量誤差在機加工以前可以被補償。試驗表明直立的加工中心的實際補償是可行的。 關(guān)鍵詞： 數(shù)控加工中心 , 熱量誤差 ,補償 0.引言： 數(shù)控機床精確度的改善是生產(chǎn)過程中質(zhì)量控制的根本。熱量誤差已經(jīng)被作為機器精確度失衡的最大誘因，而且可能也是機器獲取更高精確度的最大障礙。數(shù)控機床的熱量誤差可通過機床本身的結(jié)構(gòu)設(shè)計和生產(chǎn)技術(shù)的改善而降低。盡管如此，還是有許多物 理性限制因素使得精確度不能通過生產(chǎn)和設(shè)計技術(shù)而單獨克服。因此，誤差補償技術(shù)是很必要的。在過去的幾年里，對此技術(shù)的研究已經(jīng)獲得重大成果。由于熱量誤差在加工時隨時間而變化，許多前人的工作都集中在實際時間的的補償比率上。典型的方法是對機床幾個有代表性的點進行熱量誤差和溫度的同步試驗，然后建立一個與熱量誤差和溫度的試驗模型對多種變化進行回歸分析或是人工網(wǎng)絡(luò)分析。 在加工期間，誤差是根據(jù)之前建立的模型進行預測并通過在實際過程中用額外的信號和自由回路進行改正的。但是，目前只有很少被報道的實際過程補償案例適用于商業(yè)機床。 首先，對機床的多個點進行熱量誤差和溫度的測量是不可取的。其次，溫度傳感器的線會或多或少影響機器的運轉(zhuǎn)。第三，實際操作中的誤差補償功能在許多的機器上是不可用的。 為了改善數(shù)控機床生產(chǎn)的精確度，有個方法是值得嘗試的。盡管許多的熱源都能引起熱量誤差，但是環(huán)形軸承的摩擦被認為是最主要的熱源。熱量誤差是由 1-D 滾珠排列 來衡量的。一個自動回歸模型是以 錠子轉(zhuǎn)速然后被發(fā)展到描述那時的熱量錯誤為基礎(chǔ)的。利用這個模型，熱量誤差能夠在機械加工程序制造的時候被預測出來。通過對程序的修訂，熱量誤差能夠在加工之前得到補償。那么補償?shù)拇?價就大大的減輕了。 1.試驗工作 為了達到補償目的，重要的部分不是每個機器的零部件，而是工件的位移。在調(diào)查的線性機械加工中心中，熱量誤差是由錠子膨脹、錠子固件變形和三個軸空間的變形一起引起的。由于導桿的伸長和欄的彎曲，熱量誤差并不只是在時間上的改變，而且還是機械加工在空間上的變化。 為了能夠快速的測量熱量誤差，一些簡單的量規(guī)是可以使用的，例如：滾珠排列。滾珠排列是把一系列的滾珠按相等的間隔固定在頂梁上。由于滾珠的直徑相等，球狀的誤差比較小，因此，滾珠排列被用于熱量誤差測量的一個參考。大量的之前試驗數(shù)據(jù)表明 在光軸上的熱量誤差遠遠高于在橫軸和縱軸。所以，熱量誤差主要關(guān)注在光軸上。同理，也可以用相同的辦法得到其他兩個軸上的熱量誤差數(shù)據(jù)。測量的過程如圖 1所示：剛開始，滾珠的坐標是處在低溫狀態(tài)的，然后錠子在試驗狀態(tài)下改變機器的熱量。滾珠溫度的測量是周期性的。熱量的轉(zhuǎn)移是通過用最初的參考坐標減去在新的熱量狀態(tài)下滾珠坐標來實現(xiàn)的。由于這種測量只需要一分鐘，機器在不同坐標下的熱量轉(zhuǎn)移能夠更快更容易的被顯現(xiàn)出來。根據(jù)轉(zhuǎn)動速率的變化，熱量誤差和轉(zhuǎn)速是每十分鐘就是一個循環(huán)。坐標的唯一偏離是在低溫狀態(tài)下完成的，而不是在所關(guān)注的獨立 的量規(guī)尺寸下。象激光干涉儀這樣的精確度和準確度裝置并不做要求。只有四個測量點 z1， z2， z3， z4來覆蓋坐標為 -50， -150， -250， -350 的z 坐標的工作范圍。在其他的坐標中熱量誤差可以通過一個插值函數(shù)來獲得。 上述的試驗說明了在錠子位置和工作臺之間的派生位移與錠子和臺之間是一致的。因此熱量誤差 z 的測量反映了在真正的切割條件下誤差是可以忽略的。 為了能夠獲得機床熱量行為的全面理解以及正確的判斷誤差模型，形成了一種測量方法。錠子轉(zhuǎn)速的多種加載方式是可用的。他們被分為如下三類： 1，常規(guī)轉(zhuǎn)速， 2，轉(zhuǎn)速范圍 ， 3，真正切割狀態(tài)下的同步轉(zhuǎn)速。此處，由切割過程而引起的熱量作用沒有被考慮進來。不過，切割過程對整個機床機構(gòu)的熱量的影響在最終的過程中是可以忽略的。在這種機床中，最大的熱源來自于 z 軸。熱量誤差在 z 方向和不同的 x和 y 坐標方向大約是相同的。也就是說 x軸和 y軸的位置對 z 軸的熱量誤差沒有重大影響。 1.圖 1（左） 熱量誤差測量 錠子傳感器 2.1-D 滾珠排列 圖 2（右） 在不同 z坐標中的熱量誤差 1. z = - 50 2. z = - 150 3. z = - 250 4. z = - 350 圖 2 在測試中不同 z 坐標中熱量轉(zhuǎn)移時間過程圖的繪制 上圖表明合成的熱量轉(zhuǎn)移明顯是由所在決定的。在 z1， z2， z3， z4 點上的熱量轉(zhuǎn)移剛開始是一樣的，然后隨著時間的流逝和溫度的增加而逐漸分離。原因在于最初大量的熱量轉(zhuǎn)移是由于錠子位置的增長造成的，和其他的耐熱時間較長的機床部件相比，這個位置能更快的達到熱量平衡。然而，隨著時間的過去，那些象 導螺桿和欄這樣由位置決定熱量誤差的部件越來越多的引起合成熱量的轉(zhuǎn)移。結(jié)果，在不同的z 坐標中熱量的轉(zhuǎn)移具有不同的大小和熱量特性。但是，不同坐標中的熱量轉(zhuǎn)移是隨 z 坐標不斷改變的。 2.熱量誤差的回歸模型 熱量誤差的準確預測是精確誤差補償?shù)闹匾h(huán)節(jié)。由于對機床結(jié)構(gòu)的認識和熱源以及界限條件的不充分，根據(jù)熱量傳遞分析得出精確的數(shù)量測量是非常困難的。另外，在眾多的實用中，利用以經(jīng)驗為基礎(chǔ)的誤差模型進行回歸分析和網(wǎng)絡(luò)分析來準確預測熱量誤差是不可能的。熱量誤差是由多種熱源引起的，而只有錠子引起的熱量被認為是最重要的