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火车车轴尺寸检测系统机构设计【7张CAD图纸和说明书全稿源文件】

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火车车轴尺寸检测系统机构设计【7张CAD图纸和说明书全稿源文件】.zip

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

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火车车轴尺寸检测系统机构设计

摘要

　　　在铁路高速、重载的发展形势下，即提高铁路的运输能力，实现铁路运输的现代化，又保证铁路的运输生产安全，显得尤为重要，如果没有性能良好的车轴作为保证，要提高车辆的运行速度和运行安全是不可能的。车轴尺寸检测对检修车轴中处于关键地位，是保证车轴质量的重要手段，传统的检测方法是以人工操作为主，数据的判断读取存在较大的人为误差，所以影响了检测结果的准确性、真实性，也直接影响了车辆的行车安全。所以急需对现有的检测方式进行微机化、自动化改造，以消除测试过程中人为因素对测量结果的影响，用先进的设备保证车辆的行车安全，所以本设计研制一种火车车轴尺寸检测系统机构测量机来解决这个问题。

　　　本设计研究的车轴尺寸检测机构是一套现代化的智能检测系统，能满足火车车轴各个尺寸的检测，火车车轴尺寸测量机构主要是由侧翻机构、夹紧机构、旋转驱动、测量机构等组成，火车轴尺寸测量机改变传统人工测量的方式，实现车轴尺寸检测，避免人为测量中的误判及波动，以及误检、错检等弊端。

关键词：旋转机构；侧翻机构；顶尖

Abstract：

　　　Under the situation of the development of the railway high speed, heavy load, namely to improve railway transport capacity, realize the modernization of railway transportation, and ensure the production safety of railway transportation is particularly important, if not the good performance of the axle as a guarantee, to improve vehicle running speed and transport security is not possible. Detection of axle size of maintenance axle in the key position, is an important means to ensure the quality of the axle, the traditional detection method is mainly by manual operation, data of the judge and read there is a large man-made error, so the effect of the accuracy and reliability of the test results, has a direct impact on the safety of vehicles. So there is an urgent need to on the existing detection methods of computer and automation transformation, in order to eliminate the test in the process of human factors on the measurement results and advanced equipment to ensure the safety of vehicles, so in this paper, the development a train axle size detection system measuring machine to solve this problem.

　　　The axle dimension detection mechanism is a set of modern intelligent detection system, can meet the detection of train axles of each size, train axle dimension measuring mechanism is mainly by the rollover mechanism, a clamping mechanism, a rotating driving, measurement mechanism, shaft train size measuring machine changes the way of traditional manual measurement, size detection of axles and avoid artificial measurement error and fluctuation, and false detection, false detection of drawbacks.

Key words: Rotating mechanism；rollover mechanism；core clamper

1 绪论 1

1.1问题的提出 1

1.2 课题研究的目的与意义 2

1.3国内外现状分析 3

1.3.1国内现状分析 3

1.3.2国外现状分析 3

1.4发展趋势 4

2 火车车轴尺寸检测机构系统的方案设计 4

2.1概述 4

2.2主体设计 5

2.3火车车轴尺寸检测系统的设计方案 5

3 火车车轴尺寸检测机构液动系统设计 7

3.1液压部分设计与计算 7

3.1.1液压缸的设计计算与选取 8

3.1.2液压缸的缸筒的设计与计算 8

3.1.3活塞杆的设计与计算 9

3.1.4最小导向长度H的确定 11

4 火车车轴尺寸检测机构机械系统设计 13

4.1电机的选型设计 13

4.2顶尖心轴的设计 21

4.2.1顶尖的计算 21

4.2.2轴承的寿命计算 21

4.2.3轴承的静强度计算 23

4.2.4顶尖心轴的设计与计算 24

4.3翻转机构的设计与强度校核 25

4.4滚珠丝杠及电机选型计算 27

4.4.1确定滚珠丝杠副的导程 27

4.4.2滚珠丝杠副的载荷及转速计算 28

4.4.3滚珠丝杠副预期额定动载荷 28

4.4.4导程精度的选择 29

4.4.5丝杠电机的选择 29

5 检测部分 31

5.1激光位移传感器的简介 31

5.2激光位移传感器的工作原理 32

5.3上下滑架的设计 33

毕业设计总结 35

参考文献 36

谢辞 37

1 绪论

1.1 问题的提出

中华人民共和国铁路主要技术政策指出:

　　铁路是国家重要的基础设施,国民经济的大动脉,交通运输体系的骨干。为贯彻国家可持续发展战略,适应和促进国民经济发展和社会进步,应充分发挥铁路技术经济优势,积极发展铁路,满足运输市场需求。

　　铁路技术发展的总原则是：

　　在国家发展战略指导下,加快科技进步,突出技术创新,以市场为导向,加快科技进步,突出技术创新,以市场为导向,以经济效益为中心,以运输安全为前提,不断提高运输能力、质量和效率。坚持自主开发与引进相结合,积极采用高新技术,重视技术的综合集成。根据不同运输需求,采用不同层次的技术和装备,系统配套,发挥整体效能。改革管理体制,制订相应的政策,推动新技术尽快转化为生产力。

　　目前,我国铁路的安全技术装备落后于运输生产不断发展的要求,运量与运能的尖锐矛盾,是我国铁路存在超负荷运用铁路设备的严重倾向,长此以往,必将降低设备的安全系数,缩短使用寿命,危及行车安全。此外,铁路运输自身固有的点多、线长、生产的连续性、协作性和全天候等特点,使铁路设备也具有种类多、数量大、配置分散、连续运转、自然力影响大和有形损耗严重等特点,不仅加大了保证设备技术状态经常性良好的难度,而且还不易使运用中的设备始终处于有效的监控之下。所有这些,使铁路运输安全基础建设面临更为复杂和艰巨的挑战。发达国家铁路技术发展的实践表明,随着现代铁路高速、重载和信息技术的应用与发展,安全技术己成为与一系列高新技术相互融合、彼此渗透、不可分割的先导技术,研制和发展先进的高质量的运输基础设备和安全技术检测设备已经成为铁路现代化的重要标志。因此,在我国铁路大发展的形势下,强调运输设备的基础作用,不失时机地进行机辆设备的系统配套改进和安全技术装备的加强,改善车辆状态,改善检测装备,提高安全保障能力,必将大大加强我国铁路的安全基础设备。[10]

　　在我国铁路提速和重载和铁路信息化管理的发展过程中,车辆行驶的安全问题日益突出,对火车车轴质量也提出了更高的要求。车轴是铁路车辆上重要的运动部件,其状态直接影响到车辆的运行安全。车轴的尺寸检测至关重要，对车轴的各关键尺寸以及精度的检测非常重要,检测数据的准确性将直接影响到车轴的检修质量,当前对车轴参数的检测和数据记录基本上还是靠手工完成,测量工具采特制量具车辆车轮第四种检查器和直尺等,而这些传统的手工轮对测量装置,因效率低、差错率高、不便于信息化管理而不能满足当前的需要。与此同时,由于列车向高速重载方向发展,导致工作量加大,根据车轴检修质量控制的需要,在调研国内外同类设备技术的基础上,采用非接触的激光位移传感器检测车轴尺寸更为方便，设计这套车轴尺寸检测机。大大提高工作效率和检测精度,其应用必将为列车车轴的测量和检修提供一种高精度、高效率的检测手段,对于提高车轴的检修质量、推进铁路系统的计算机管理、保障铁路机车的安全运行具有重要的现实意义。

1.2 课题研究的目的与意义

　　随着科学技术的不断创新、生产水平的加速进步，要求机械装置的精度、速度也越来越高。轴类相关零件主要起到传递转矩和运动的作用，并保证各部件性能的稳定性，是各种机械装置中应用范围最广、应用面最大、应用程度最高的一种零件，诸如各类变速箱、汽车发动机及传动部件、火车轮对系统等中都有轴类零件重要应用。鉴于轴类零件的重要应用场合，对其相关参数的高精度测量及检测将对人员、生产安全等起到保障作用，《铁路货车轮对和滚动轴承组装检修规则》中规定：货车轮对检修压装过程中轴颈的检测工作量很大，一般情况下，一个车辆段一天检修检测50～100个轮对轴颈。另外，轴承内圈与轴颈之间存在过盈配合，涉及到轴承与轴颈的测量精度问题，而轴承作为标准件，在制造出厂时已标注其精度，因此轮对轴颈轴径测量存在必要性。目前，轴类零件的测量方法可分为机械测量法、气动法、超声法、光学检测法等，传统的机械测量法大都利用机械式测量工具，利用直尺、游标卡尺、千分尺等工具进行机械接触式测量方法容易使零件表面发生损伤或变形。气动法、超声法等手工见表面粗糙度影响较大，线形范围小，且精度不高，测量范围小而有限，在实际测量检测中得不到较广的发展空间，应用较少。机器视觉测量技术作为一种非接触的、高精度的柔性坐标测量技术，可以满足现代化制造领域对检测技术及系统新的更高的技术需要，文章基于机器视觉测量原理对轴径测量装置进行设计研究，通过模拟装置进行实际测量、试验验证，数据显示，此激光视觉轴径测量装置的测量方法简单、测量精度高，且适用于大尺寸轴径测量。 QQ截图20200308111433.png

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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: 10.1243/09544054JEM1932 2010 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 method Published by: On behalf of: Institution of Mechanical Engineers can 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 thecuttingprocess. Thecuttingprocessisnottreatedlikeatheoreticalprocess, whichisobviousbythe 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, HilbertHuangtransform1INTRODUCTIONThe 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 aswellasinthepresenceofthecoolantfluidsonacuttertool.The Fourier transform (FT) and its modified short-time Fourier transform has been widely studied inordertodetectcuttertoolwearorcuttertoolbreak3.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 ManufactureJEM1932 by guest on January 9, 2013Downloaded from Cutter tool fault detection using a new spectral analysis method1785analyse 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 7.2EXPERIMENTAL METHODS2.1Tool 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 wearortoolbreak)ofthecuttinggeometrycanbeobservedin the spectral analysis.The physical essence of the cutter tool wear will beneglected in the following parts of this study.2.2The 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,anycomplicateddatasetcanbedecomposedintoafiniteandoftensmallnumberofcomponents:acollection of intrinsic mode functions (IMF). An IMFrepresents a generally simple oscillatory mode as acounterparttothesimpleharmonicfunction.Inorderto 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 is ?1;(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) =n?j=1cj+ rn(3)AnotherstepistoapplytheHilberttransformtothedecomposed IMFs. Each component has its Hilberttransform yiyi(t) =1?cj()t d(4)JEM1932Proc. IMechE Vol. 224 Part B: J. Engineering Manufacture by guest on January 9, 2013Downloaded from 1786T Kalvoda, Y-R Hwang, and M VrabecFig.1Cutting 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) =?x2+ y2,(6)and(t) = arctan(y/x)(7)Here,a(t)istheinstantaneousamplitudeand(t)isthe phase function, and the instantaneous frequencyis simply =ddt(8)AfterperformingtheHilberttransformoneachcomponent,theoriginaldatacanbeexpressed as the real part Rin the followingformx(t) = ?n?j=1aj(t)exp?i?j(t)dt?(9)With the Hilbert spectrum defined, the marginalspectrum can be defined ash() =T?0H(,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 ManufactureJEM1932 by guest on January 9, 2013Downloaded from Cutter tool fault detection using a new spectral analysis method1787Table 1Cutting conditionsCuttingSpindleCutter toothFeedDepthWidthspeedrevolutionfrequencyrateof cutof cutTestVc(m/min) (r/min)ft(Hz)f (m/min)ap(mm)ae(mm)174.841985132.331.0511.51.2250.7134589.670.47811to 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, whichcomparestheresultstotherealsignal(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 byincreasingpowerdensity3,ratherthanbyfrequencychange.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-stationarydata. Themostseriousweaknessofwaveletanalysis is again the limitation imposed by the uncer-tainty principle (product of the frequency resolution,?,andthetimespanoverwhichthefrequencyvalueis defined, ?T, 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)describethefrequencychangingwithinonepeak.2.3Experimental 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 thenaturalfrequencyofthetoolwasalsomeasured. Inallcases 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 onJEM1932Proc. IMechE Vol. 224 Part B: J. Engineering Manufacture by guest on January 9, 2013Downloaded from 1788T Kalvoda, Y-R Hwang, and M VrabecFig.2Experimental cutting set-upFig.3Segment 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.3RESULTS3.1Time 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.4Segment 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 ManufactureJEM1932 by guest on January 9, 2013Downloaded from Cutter tool fault detection using a new spectral analysis method1789Fig.6Decomposed 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.3.2Time-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-axis in coordi-nates of the milling machine) are presented.Figure 5(b) shows the Hilbert spectra of the dam-aged cutter tool. The damage to the cutter toolwas simulated by grinding one of the teeth into atriangle shape. The change of the instantaneous fre-quency is obvious. The drift into lower frequencies aswell as the higher fluctuations of the instantaneousfrequency indicates the cutter tool damage.The most obvious indicator of the cutter tool breakis the appearance of the new frequency around 32Hz.Thisfrequencycorrespondsexactlytothegap(causedby cutter tool fault) in the time domain set of thedamaged cutter tool in Fig. 3. The gap is marked inFig. 3 as a grey rectangle.The EEMD (equations (1) to (3) allows the fre-quency separation, which works like a band-passfilter. This has advantage that the filter does nothave to be used. The two significant components ofthe decomposed signal of the damaged cutter toolare shown in Fig. 6. Those frequencies correspondto the highest energy in Hilbert spectra (Fig. 5(b).The IMFs components are sorted from the highestfrequency into lowest frequency.The advantage of the data presentation in thetime-frequency domain is very straightforward. Thedifference between the Fourier transform and HHTis obvious: results can be presented in the in time-frequency domain at a high-frequency resolution,which is impossible using Fourier transform.3.3Frequency domainFigure 7 shows the Hilbert marginal spectra, whichis similar to the power spectra for Fourier transform.Figure 7 corresponds to Figs 5(a) and 5(b), but thepresentation is in the frequency-power spectra. Thetool fault is also obvious. The tool fault indicates thehigher energy in the Hilbert marginal spectra aroundthe frequency 32Hz, which also correspond to theJEM1932Proc. IMechE Vol. 224 Part B: J. Engineering Manufacture by guest on January 9, 2013Downloaded from 1790T Kalvoda, Y-R Hwang, and M VrabecFig.7Marginal spectra: comparison between new anddamaged cutter tool, test 1Fig.8Marginal spectra: comparison between new anddamaged cutter tool, test 2gap in Fig. 3. The shift into lower frequencies iscaused by tool wear. The change in the shape of thecuttertoothintoadifferentgeometrycausesthesignalto shift into lower frequencies.The new method was used for different cuttingconditions, test 2, (Table 1) in order to confirmthe repeatability. The results (Fig. 8) correspond totheresultsabove:ashiftofthetrackedpeakintolowerfrequency and new frequency occurrence as a resultof the cutter tool fault.Figure 9 shows the comparison of the Hilbertmarginal spectra and the Fourier transform. Theadvantage is obvious: the Hilbert marginal spectrumdoes not have any harmonic it presents the datalocally, and the slight oscillations around the funda-mental frequency present the cutting process muchmore reliably.Fig.9Welchs power spectrum and marginal spectrumof the undamaged cutter tool (estimated from thedynamic force), test 14CONCLUSIONS1. The novel approach in order to detect the cuttertool fault has been presented.2. The assumption of the cutting process non-linearity and non-stationarity was taken intoaccount.3. Tool fault detection is bases on the appearanceof the other fr

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