1284-滾筒采煤機截割部的設(shè)計

1284-滾筒采煤機截割部的設(shè)計,滾筒,采煤,機截割部,設(shè)計 IMPROVING ACCURACY OF CNC MACHINETOOLS THROUGH COMPENSATIONFOR THERMAL ERRORSAbstract： 　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 　　Compensation0 　INTRODUCTIONImprovement 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 WORKFor 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 errorsΔ 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 andidentify 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 = - 350Fig.2 plot s the time-history of thermal drift Δ z at different z coordinates under a test . Itshows 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 ERRORPrecise 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. Thevalue 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 formwhere 　 Δ z ( t) ———Thermal error at time tk , m ———Order of the modelai , bi ———Coefficient of the modeln ( t - i) ———Spindle rotation speed at time t - iThe order k and m are determined by the final prediction-error criterion. The coefficients aiand 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 results3 　PRE-COMPENSATION FOR THERMAL ERRORSThe 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 Δ z are calculated by the model. Then the program is corrected by adding the calculated Δ 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 7μm to 2μm.Fig.5 　Compensation for thermal errors by revising machining programFig.6 　The effectiveness of compensation4 　CONCLUSIONSA 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.References1 　Chen J S , Chiou G. Quick testing and modeling of thermally-induced errors of CNC machine tools. InternationalJournal of Machine Tools and Manufacture , 1995 , 35(7) ∶1 063～1 0742 　Chen J S. Computer-aided accuracy enhancement for multi -axis CNC machine tool. International Journal of Machine Tools and Manufacture , 1995 , 35(4) ∶593～6053 　Donmez M A. A general methodology for machine tool accuracy enhancement by error compensation. Precision Engineering , 1986 , 8 (4) ∶187～1964 　Lo C H. An application of real-time error compensation on a turning center. International Journal of Machine Tools and Manufacture , 1995 , 35(12) ∶1 669～1 682.5 　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) ∶527～5376 　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