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数控铲磨床纵向进给系统的设计【11张CAD图纸和说明书】

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数控铲磨床纵向进给系统的设计【11张CAD图纸和说明书】.zip

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Cutter tool fault detection using a new spectral.pdf---(点击预览)

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关键词：: 数控磨床纵向进给系统设计 11张 cad 图纸说明书

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摘要

铲磨床是滚刀、成型铣刀等复杂刀具的精加工机床。由于传统铲磨床加工精度下降、生产效率低，工人劳动强度大，本设计对传统铲磨床的纵向进给机构进行数控化改造来改善其上述不足之处。

通过对目前工厂中传统的铲磨床进行研究，参考数控机床的相关文献，了解铲磨床纵向进给部分的工作原理，并深入分析铲磨床在加工的过程中各个零部件的受力情况，按寿命计算选择了丝杠的尺寸规格，并校核了额定动载荷、传动效率、刚度，最终选择了汉江机床厂生产的滚珠丝杠。通过对主轴受力的分析选择了用推力球轴承承受轴向力，用深沟球轴承承受径向力的形式。导轨的选取参考了汉江机床厂生产的滚动导轨，对导轨的寿命以及额定载荷进行了校核，均能满足要求。电机根据滚珠丝杠的导程计算出的最高转速，和传动过程中的最大转矩选取了富士公司的伺服电机并对转动惯量进行了校核。由于采用了闭环系统，在查阅了光栅尺的相关参数后，选择FAGOR公司的光栅尺能使在规定的行程内定位分辨率达到要求。

通过上述设计实现了铲磨床纵向进给系统的数控化改造，满足了加工精度的要求，具有加工稳定可靠，效率高等优点。

关键词: 数控铲磨床;纵向进给系统;精加工;闭环系统

Design of Longitudinal Feed System of CNC Relief

Grinding Machine

Abstract

CNC relief grinding machine is a complex tool finishing machine for hobbing cutter, formed mill cutter. Because traditional relief grinding machine’s accuracy and production efficiency is low, the workers labor intensity is too big .The purpose of the numerical control reformation for the longitudinal feed system of traditional relief grinding machine is to improve the performance.

After the traditional relief grinding machine had been researched in the factory at present, and reference related literature of CNC machine tools, The longitudinal feed part of relief grinding machine of working principle, and in-depth analysis of relief grinding machine in the process of machining force situation of every parts and components.

According to the life of screw，chosed the size of screw. And checked the dynamic load rating, transmission efficiency, stiffness. Ultimately chose the ball screw produced by Hanjiang Machine Tool Factory . Through the analysis of the force acting on the spindle. A thrust ball bearing under axial force had been chosed, and a deep groove ball bearings bear radial force. With reference to the rolling guide produced by Hanjiang Machine Tool Factory, and calculating the life of rail and rated load, All parameters of guide can satisfied the requirements. According to the highest speed of the motor which is calculated according to the lead of ball screw and maximum torque of transmission process, The servo motor Fuji Corp had been chosed. And the moment of inertia is checked. Due to the adoption of the closed-loop system, the related parameters of grating ruler lookup, The grating ruler produced by FAGOR company had been chosed, which can satisfied that Positioning resolution meet the requirements stipulated in the distance.

Through the design , Implementation of the NC transformation of relief grinding machine longitudinal feed system, It meet’s the requirement of processing precision , and the processing is stable and reliable, high efficiency.

Key words:CNC relief grinding machine; Longitudinal feed system; Finish machining;Closed loop system

1 绪论 1

1.1概述 1

1.2数控机床的优点 2

1.3数控机床的组成 3

2 总体方案设计 7

2.1机床的运动关系 7

2.2传动方案的设计 7

2.2.1丝杠的选型及支撑方式的设计 7

2.2.2检系统的选取 8

2.2.3导轨的选定 8

2.2.4丝杠和电机连接零件的选取 9

2.2.5轴承类型的选取 9

3 进给伺服系统机械部分计算与校核 10

3.1 滚珠丝杠螺母副的计算和选型 10

3.1.1额定动载荷 10

3.1.2传动效率校核 12

3.2轴承的计算和选型 12

3.2.1推力球轴承的选型 12

3.2.2深沟球轴承的选型 13

3.3丝杠的刚度和稳定性校核 14

3.3.1 丝杠的刚度校核 14

3.3.2稳定性校核 15

3.4导轨的计算和选型 15

3.4.1滚动直线导轨副行程长度的寿命 15

3.5 伺服电机的计算和选型 16

3.5.1电机转速的选取 17

3.5.2电机转矩的计算 17

3.5.3转动惯量的校核 18

3.6 编码器的选型 18

4 进给系统机械部分结构设计 19

4.1进给伺服系统装配图的设计 19

4.2安装过程中应注意的问题 19

5 总结 21

参考文献 22

致谢 23

毕业设计（论文）知识产权声明 24

毕业设计（论文）独创性声明 25

1 绪论

1.1概述

我国目前机床总量为380万余台，而其中数控机床总数只有11.34万台，这说明我国机床数控化率不到3%。我们大多数制造业和企业的生产、加工设备大多数是传统机床，而且半数以上是役龄在10年以上的旧机床。用这种机床加工出来的产品普遍存在质量差、品种少、成本高等缺点，因此这些产品在国际、国内市场上缺乏竞争了，这直接影响了企业的生存和发展。所以必须提高机床的数控化率。

在过去的几十年，虽然金属切削的基本原理变化不大，但随着社会生产力的发展，要求制造业向自动化和精密化方向发展，而刀具材料和电子技术的进步，特别是微电子技术、电子计算机的技术进步，运用到控制系统中，既能帮助机床的自动化，又能提高加工精度。这些都要求对旧机床进行改造。另外，在经济方面，用机床的数控改造比更新设备节约50%的资金。再加上市场的原因，由于目前机床市场供给无法满足大量的机床设备的更新需要，因此更显示出机床数控改造的必要性。

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内容简介：: /ManufactureEngineers, Part B: Journal of Engineering Proceedings of the Institution of Mechanical/content/224/12/1784The online version of this article can be found at:DOI: 10.1243/09544054JEM19322010 224: 1784Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering ManufactureT Kalvoda, Y-R Hwang and M VrabecCutter tool fault detection using a new spectral analysis methodPublished by:On behalf of:Institution of Mechanical Engineerscan be found at:ManufactureProceedings of the Institution of Mechanical Engineers, Part B: Journal of EngineeringAdditional services and information for /cgi/alertsEmail Alerts: /subscriptionsSubscriptions: /journalsReprints.navReprints: /journalsPermissions.navPermissions: /content/224/12/1784.refs.htmlCitations: What is This?- Dec 1, 2010Version of Record by guest on January 9, 2013Downloaded from 1784Cutter tool fault detection using a new spectralanalysis methodgT Kalvoda1*, Y-R Hwang1,2, and M Vrabec31Department of Mechanical Engineering, National Central University, Chung-Li, Taiwan, Republic of China2Department of Mechanical Engineering and the Institute of Opto-Mechatronics Engineering, NationalCentral University, Chung-Li, Taiwan, Republic of China3Faculty of Mechanical Engineering, Czech Technical University of Prague, Prague, Czech RepublicThe manuscript was received on 10 December 2009 and was accepted after revision for publication on 22 March 2010.DOI: 10.1243/09544054JEM1932Abstract: An investigation of milling end cutter tool fault monitoring based on dynamic forcein the frequency domain and time-frequency domain is presented in this paper. A new dataanalysis technique, the HilbertHuang transform (HHT), is used to analyse this process inthe frequency domain and time-frequency domain. This technique is also compared with thetraditional Welchs method power spectra based on the Fourier transform (FT) in the frequencydomain approach. The non-linearity and non-stationarity of the cutting process are taken intoaccount. This method is designed to track the main peak in the frequency domain and time-frequency domain (HHT). The main tool break indicator is the appearance of new frequency asa result of the cutter tool fault. The HHT analysis technique covers the physical nature of thecutting process. The cutting process is not treated like a theoretical process, which is obvious bythe oscillation of the frequency around the fundamental frequency of the cutter tool. The breakof the cutter tool is obvious in the presented results.Keywords: cutter tool fault, spectral analysis, milling process monitoring, HilbertHuangtransform1 INTRODUCTIONThe computer numerical control (CNC) machinescannot detect cutter tool conditions in an on-linemanner. Because a broken tool may continue func-tioning without being detected, the materials costswill increase and the quality of products will dimin-ish as errors are made by the broken tool in process.To reduce the materials costs and prevent dam-age to the cutting tool, detecting technology of anunmanned, on-line tool breakage detection system isnecessary 1.The tool wear monitoring has been widely studiedby many different approaches. There are two majorapproaches using sensing technology for detectingtool breakage: one is the direct method, which mea-sures and evaluates the volumetric change in the*Corresponding author: Department of Mechanical Engineer-ing, National Central University, No. 300, Jhongda Road,No. 300, Jhongda Road, Chung-Li, Taiwan, Republic of China.email: kalvodatool, and the other is the indirect method, whichmeasuresthecuttingparametersduringtheoperationprocess 2.The disadvantage of the direct processes is obviousin terms of the interruption of the cutting process aswell as in the presence of the coolant fluids on a cuttertool.The Fourier transform (FT) and its modified short-time Fourier transform has been widely studied inorder to detect cutter tool wear or cutter tool break 3.The lack of this method leads to the assumption thatthe processed data are strictly linear and stationary,which is impossible owing to the nature of the cut-ting process. Another shortcoming of the FT is thepresence of harmonics as a multiple of fundamentalfrequency, which makes it difficult to recognize thereal frequency from harmonic. The Fourier transformpresentation is limited to the frequency domain.Thepossibledirectionofthestudytoolwearprocessor cutter tool break provides the wavelets trans-form 3,4, but the assumption of the data linearityfor wavelet transform makes it difficulty to reliablyProc. IMechE Vol. 224 Part B: J. Engineering Manufacture JEM1932by guest on January 9, 2013Downloaded from Cutter tool fault detection using a new spectral analysis method 1785analyse the dynamic cutting force signal in order tomonitor the cutting process.The new method HilbertHuang transform (HHT)for time series analysis was proposed 5,6. Themethod overcomes the shortcomings of non-linearityand non-stationarity of the time series data sets. TheHHT was successfully applied for many solutions oftime series analysis: structural health monitoring,vibration, speech, bio-medical applications, and soon 6. The HHT consists of two fundamental steps:signal decompositions using empirical mode decom-position (EMD), which is actually a dyadic filter bank,and the instantaneous frequency computation 7.2 EXPERIMENTAL METHODS2.1 Tool wear recognitionThe tool wear is generally caused by a combinationof various processes. Tool wear can occur graduallyor in drastic breakdowns. Gradual wear may occur byadhesion, abrasion, or diffusion, and it may appearin two ways: wear on a tools face or wear on itsflank. Contact with the chip produces a crater in thetool face. Flank wear, on the other hand, is com-monly attributed to friction between the tool and theworkpiece material. In general, increasing the cut-ting speed increases the temperature at the contactzone, leading to a drastic reduction of the tools life.The milling cutting process is specified by theintensive contact between the cutter tool and theworkpiece and it leads to the tool wear or tool break-age. The described process is characterized by thechange of the cutter tool geometry. The cutting toothinduces the fluctuation part in the cutting force as aresult of the forced vibration. The change (tool wearor tool break) of the cutting geometry can be observedin the spectral analysis.The physical essence of the cutter tool wear will beneglected in the following parts of this study.2.2 The HilbertHuang transform as a methodof analysisThe limitation of use of the traditional methods suchFourier and wavelet transforms was presented above.Recent research 5,6 has brought a new approachfor non-linear and non-stationary data. The HHT hasbeen shown to perform well for these kind of data.The HHT has been successfully applied for manysolutions of non-linear and non-stationary data. Thepresentation in both frequency and time-frequencydomainsshowstheadvantageoftheothertransforms.The important event in the cutting process may beattributed to given time.The EMD method is fundamental to HHT. Usingtheensembleempiricalmodedecomposition(EEMD)method, anycomplicateddatasetcanbedecomposedinto a finite and often small number of components: acollection of intrinsic mode functions (IMF). An IMFrepresents a generally simple oscillatory mode as acounterpart to the simple harmonic function. In orderto avoid mode mixing between the individual compo-nents, the white-noise of the given value is added intothe investigated signal (this process is referred to asEEMD). By definition, an IMF is any function with thesame number of extrema and zero crossings, with itsenvelopes being symmetric with respect to zero 5,6.The process of EMD is as follows:(a) identify minima and maxima;(b) connect local minima and maxima using thespline;(c) find the mean (m1) of the upper and bottomenvelope identification.The mean is designated as m1, and the differencebetween the data and m1in the first component h1ish1= x(t) m1(1)In the second sifting process, h1is treated as thedata, thenh1 m11= h11(2)Thissiftingprocedurecanberepeatedk times, untilh1kis an IMF, that is h1(k1) m1k= h1k; thenit is designated as c1= h1k, the first IMF compo-nent from the data. To check if h1kis an IMF, thefollowing conditions must be fulfilled 5,6:(a) the difference between the numbers of extremaand zero-crossings islessorequalslant1;(b) the mean of the upper envelope (linked by localmaxima) and the lower envelope (linked by localminima) is zero at every point.The first IMF c1is subtracted from the original sig-nal r1= s c1. This difference is called the residuer1. It is now treated as the new signal and subjected tothe same sifting process. The decomposition processfinally stops when the residue rnbecomes a mono-tonic function or a function with only one extremumfrom which no more IMF can be extracted. Decom-position of the original signal into n-empirical modesand a residue is then achieved byx(t) =nsummationdisplayj=1cj+ rn(3)Another step is to apply the Hilbert transform to thedecomposed IMFs. Each component has its Hilberttransform yiyi(t) =1integraldisplaycj()t d (4)JEM1932 Proc. IMechE Vol. 224 Part B: J. Engineering Manufactureby guest on January 9, 2013Downloaded from 1786 T Kalvoda, Y-R Hwang, and M VrabecFig.1 Cutting force signal analysed by using of various approaches: (a) original data set; (b) Fouriertransform of the signal; (c) wavelet transform; (d) HHT of the original signalWith the Hilbert transform, the analytic signal isdefined asz(t) = x(t) + iy(t) = a(t)ei(t)(5)wherea(t) =radicalBigx2+ y2, (6)and(t) = arctan(y/x) (7)Here, a(t)istheinstantaneousamplitudeand(t)isthe phase function, and the instantaneous frequencyis simply =ddt(8)After performing the Hilbert transform oneach component, the original data can beexpressed as the real part R in the followingformx(t) =Rfracturnsummationdisplayj=1aj(t)expbracketleftbiggiintegraldisplayj(t)dtbracketrightbigg(9)With the Hilbert spectrum defined, the marginalspectrum can be defined ash() =Tintegraldisplay0H(,t)dt (10)The marginal spectrum offers a measure of thetotal amplitude (or energy) contribution from eachfrequency value. This spectrum represents the accu-mulated amplitude over the entire data span in aprobabilistic sense. All details of HHT are given inreferences 5 and 6.The performance of the Fourier transform, wavelet,and HHT can be demonstrated by an artificial sig-nal. The signal corresponds to the cutting force in thex-axis (Fig. 1(a). The cutting conditions correspondProc. IMechE Vol. 224 Part B: J. Engineering Manufacture JEM1932by guest on January 9, 2013Downloaded from Cutter tool fault detection using a new spectral analysis method 1787Table 1 Cutting conditionsCutting Spindle Cutter tooth Feed Depth Widthspeed revolution frequency rate of cut of cutTest Vc(m/min) (r/min) ft(Hz) f (m/min) ap(mm) ae(mm)1 74.84 1985 132.33 1.05 11.5 1.22 50.7 1345 89.67 0.478 1 1to test 1 given in Table 1; a low carbon steel wasconsidered for the cutting force simulation. The con-stants for the cutting force simulation are adoptedfrom reference 8.The presentation of the comparisons (Figs 1(b),(c), (d) is given in the time-frequency domain, whichcompares the results to the real signal (Fig. 1(a) betterthan in frequency domain.Figure 1(b) shows the time-frequency presentationusing Fourier transform (Fig. 1(b) for a non-linearbut weak stationary signal. Figure 1(b) shows thefundamental frequency around 132Hz with threeharmonics as a multiple of the fundamental fre-quency. The presence of the harmonics is typicalfor asymmetric signals. It does not have any phys-ical meaning in this case. With Fourier transformthe frequency values are constant over the wholetime span covering the range of integration. As theFourier definition of frequency is not a function oftime, it can be easily seen that the frequency con-tent would be physically meaningful only if the datawere linear and stationary. That is why a cutter toolfault by use of Fourier transform was studied byincreasing power density 3, rather than by frequencychange.Continuous wavelet transform (Fig. 1(c) wasapplied to the same data set (Fig. 1(a). The waveletis extremely useful for data comparison and imageprocessing. The wavelet approach offers the time-frequency information with an adjustable window.The frequency is actually pseudo frequency. Therepresentation is usually shift-scale. The scale isproportional to the frequency and shift to time. Thelocal property of the wavelet allows a change in thefrequency to be detected, so it is useful for non-stationary data. The most serious weakness of waveletanalysis is again the limitation imposed by the uncer-tainty principle (product of the frequency resolution,Delta1, and the time span over which the frequency valueis defined, Delta1T, shall not be less than 1/2) to be localand a base wavelet cannot contain too many waves;yet to have fine frequency resolution, a base waveletwill have to contain many waves 7.Figure 1(c) shows very obvious peaks, and thefrequency corresponds to the theoretical frequency132Hz. Figure 1(d) shows the results computedusing HHT. The continuous frequency along thetime line is obvious. The process of computing thetime-frequency domain is based on equations (1)to (10); however, the instantaneous frequency canbe computed based on the Hilbert transform, zerocrossings, or quadrature reference 7. The conceptof the instantaneous frequency computation allowsfrequency to be computed not only in the distancebetween the two peaks, but also within one peakif the data density is high enough. The oscillations(Fig. 1(d) describe the frequency changing within onepeak.2.3 Experimental equipment and designThe material used for the workpiece in the testwas SAE 1045 carbon steel with a nominal mate-rial composition of C = 0.45per cent, Mn =0.75per cent, P = 0.04per cent max, S =0.05per cent max (wt%). The cutting tool selectedwas an end-mill type manufactured from high-speed steel (HSS-Co), with a diameter of 12mm, andfour flutes. The test was performed on a five contin-uous axis milling machine centre, manufactured byChuan Liang, having a maximum spindle revolutionof 20000r/min, with a NUM 760 control system. TheNC program was created in Cam NX 4.0. Dry cuttingwas performed.The experiment was performed for two differentcutting conditions given in Table 1. Each test wasrepeated at least four times.In order to avoid misrepresentation of the toolvibration with the natural frequency of the cuttingset-up an impact test was performed. The naturalfrequency was measured by the impact hammer inall directions on the workpiece (x, y, z) and thenatural frequency of the tool was also measured. In allcases the frequencies were higher than 2.5kHz. Thethree-axis piezoelectric dynamometer Kistler 9257Bwas connected to the charge amplifier.Vibration was picked up by the data acquisitionunit instruNET (OMEGA). The data acquisition unitwas connected to a PCI Bus controller card for a PC.The sampling rate was 5kHz. The data were pro-cessed using Matlab. The experimental cutting set-upis shown in Figure 2.All signals were recorded under loading. Somesignals were collected from different positions onJEM1932 Proc. IMechE Vol. 224 Part B: J. Engineering Manufactureby guest on January 9, 2013Downloaded from 1788 T Kalvoda, Y-R Hwang, and M VrabecFig.2 Experimental cutting set-upFig.3 Segment of cutting force data set damaged cuttertool, time domainthe workpiece. The cutter tooth frequency ftwascalculated using the following equationft=60n (11)where is the spindle speed in r/min and n is thenumber of teeth on the cutter tool.3 RESULTS3.1 Time domainFigure 3 shows a segment of the data set of thedamaged cutter tool in time domain in contrast toFig. 4, where the cutter tool was undamaged. TheFig.4 Segment of cutting force data set undamagedcutter tool, time domain(a)(b)Fig.5 (a) Hilbert spectrum of the undamaged cutter tool.(b) Hilbert spectrum of the damaged cutter toolProc. IMechE Vol. 224 Part B: J. Engineering Manufacture JEM1932by guest on January 9, 2013Downloaded from Cutter tool fault detection using a new spectral analysis method 1789Fig.6 Decomposed signal of the damaged cutter tool, the highest energy componentsresults correspond to the cutting conditions for test1, given in Table 1. The change of the amplitudein every fourth peak corresponds to the cutter toolbreak (Fig. 3). The speed of the spindle was =1985r/min; thus the frequency of each tooth was:ft= 33Hz (equation (11). The cycle is marked inFig. 3. The marked period (0.032s) roughly corre-sponds to the cycle of one spindle revolution if theperiod is computed (equation (11). The presenteddata segment, however, does not represent all ofthe data set; most of the time the signal is not verysteady and tool break estimation could therefore beimpossible.The shortcoming of the Fourier transform for thepresented data set (see section 2.2) is obvious. Timedomain representation does not show all of the con-tent of the data set. Therefore a better representationis given in the frequency domain or time-frequencydomain.3.2 Time-frequency domainFigure 5(a) shows Hilbert spectra of the undam-aged cutter tool. The novel approach (HHT) showsoscillations around the fundamental frequency ofthe forced vibrations, which was 132Hz. Theinstantaneous frequency can be computed by usingHHT. The slight oscillations describe the cutting pro-cess better. The cutting process by using HHT is nottreated like a theoretical process.The results of the radial force Fr(x-

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