喜歡這套資料就充值下載吧。。。資源目錄里展示的都可在線預覽哦。。。下載后都有，，請放心下載，，文件全都包含在內(nèi)，圖紙為CAD格式可編輯，【有疑問咨詢QQ:414951605 或 1304139763】 機械加工工藝過程卡片 （廠 名） 機械加工工藝過程卡片 產(chǎn)品型號 零件圖號 產(chǎn)品名稱 三孔連桿 零件名稱 三孔連桿 共 1 頁 第1頁 材料牌號 45 毛坯種類 鍛造 毛坯外型尺寸 每毛坯可制作件數(shù) 1 每臺件數(shù) 1 備注 工序號 工序名稱 工序內(nèi)容 車 間 工 段 設(shè) 備 工藝裝備 工 時 準終 單件 10 鍛 鍛造毛坯 鍛造車間 一 20 退火 退火（消除內(nèi)應力） 鍛造車間 一 30 粗銑 粗銑φ112、φ50圓柱端面一側(cè)端面，留加工余量 機加工 二 X52K銑床 銑夾具，量具，銑刀 40 粗銑 粗銑φ25孔端面一側(cè)端面，留加工余量 機加工 二 X52K銑床 銑夾具，量具，銑刀 50 粗銑 翻面，粗銑φ112、φ50圓柱端面另外一側(cè)端面 機加工 二 X52K銑床 銑夾具，量具，銑刀 60 粗銑 粗銑φ25孔端面另外一側(cè)端面，留加工余量 機加工 二 X52K銑床 銑夾具，量具，銑刀 70 熱處理 熱處理226-271HBS 80 精銑 精銑φ112、φ50圓柱端面一側(cè)端面 機加工 二 X52K銑床 銑夾具，量具，銑刀 90 精銑 精銑φ25孔端面一側(cè)端面 機加工 二 X52K銑床 銑夾具，量具，銑刀 100 精銑 翻面，精銑φ112、φ50圓柱端面另外一側(cè)端面 機加工 二 X52K銑床 銑夾具，量具，銑刀 110 精銑 精銑φ25孔端面另外一側(cè)端面 機加工 二 X52K銑床 銑夾具，量具，銑刀 120 粗鏜 粗鏜φ90孔，留加工余量 機加工 二 T68鏜床 鏜夾具，鏜刀 130 鉆擴鉸孔 鉆擴鉸φ35孔 機加工 二 Z525鉆床 鉆夾具，鉆頭 140 鉆擴鉸孔 鉆擴鉸φ25孔 機加工 二 Z525鉆床 鉆夾具，鉆頭 150 精鏜 精鏜φ90孔并倒角 機加工 二 T68鏜床 鏜夾具，鏜刀 160 鉗工 去毛刺 170 檢驗 檢驗 設(shè)計（日期） 審核（日期） 標準化（日期） 會簽（日期） 標記 處數(shù) 更改文件號 簽字 日期 標記 處數(shù) 更改文件號 簽字 日期 描圖 描校 底圖號 裝訂號 畢業(yè)設(shè)計需完成的設(shè)計工作 1.根據(jù)工程圖進行三維建模 將給定零件的工程圖，進行三維建模，可以采用UG、Pro/e或者其他三維軟件。 2.零件機械加工工藝編制 2.1 毛坯確定 毛坯制備方法的選擇(毛坯制備方法有鑄造、鍛造、焊接、型材等大類，在大類中根據(jù)零件的要求及數(shù)量，會選擇細分制備方法)。 毛坯圖繪制(用AutoCAD繪制，標注尺寸)。 2.2零件加工工藝的編制(按給定工藝卡編制)。 此過程需嚴格根據(jù)工藝編制的程序，并記錄其中的工藝選擇的過程及理由(此內(nèi)容最后需寫入畢業(yè)論文)。 工藝編制的基本過程： 第一步，零件圖審核(主要是加工工藝性審核)，審核零件結(jié)構(gòu)是否便于加工、零件加工精度是否合適、零件的技術(shù)要求是否合適、尺寸是否其全、零件的裝配性。特別是對主要尺寸的精度應該有比較深入的討論，即這個精度一般的機械加工是否能達到。 第二步，毛坯的確定 已由前面確定，只要記錄其中的理由即可。 第三步，工藝路線擬定(工藝編制的重點) (1)確定主要尺寸的加工路線(根據(jù)零件的主要尺寸，確定加工路線) 根據(jù)，零件主要尺寸的加工精度要求，選擇合適的經(jīng)濟加工精度，確定加工工藝路線，計算每一工序的工序尺寸。 (2)確定各個工序的定位基準，設(shè)計各個工序的定位方案，分析計算各個工序的定位誤差。(此部分是論文的核心之一，對其中是的每一個方案、計算過程都必須有詳細的記錄，畢業(yè)論文中需要這些)。 (3)選擇其中一個工序，進行工序卡的編制和專用夾具的設(shè)計。(具體做什么工序，需同老師討論確定) 第四步，工裝、設(shè)備的選擇 列出需要的工裝、設(shè)備，記錄一定的理由。 第五步，切削用量確定 (1)確定每一工序的切削用量。 (2)詳細確定指定工序的切削用量。 此步驟主要是記錄切削用量選擇的理由。 第六步，工時定額計算 參考工藝編制手冊即可。 第七步，技術(shù)經(jīng)濟分析 主要對多個方案進行經(jīng)濟比較，可以參考相關(guān)書籍。 第八步，選擇最佳方案 通過技術(shù)、經(jīng)濟方案的比較，獲得最佳方案。 第九步，填寫工藝文件 (1)零件的加工工藝過程卡(提供參考模板)。 (2)指定工序的工序卡(提供參考模板)。 說明：若某一工序采用數(shù)控加工的方式，同樣需要進行數(shù)控加工工藝的編制，并提供數(shù)控加工程序、模擬仿真結(jié)果。 3.指定工序夾具設(shè)計 夾具設(shè)計的過程見參考文檔。 要求夾具設(shè)計需要完成如下內(nèi)容： (1)夾具設(shè)計方案 包括定位方式、夾緊方式、對刀方式，及與其他設(shè)備聯(lián)接方式選擇等。定位誤差計算(在工藝編制時，這個工序的可以略寫)。 (2)夾具裝配圖。 (3)所有非標準件的零件圖。 (4)夾具設(shè)計說明。 (5)夾具三維模型。 4.畢業(yè)論文(工藝和夾具設(shè)計完成撰寫) (1)工藝編制說明。 (2)夾具設(shè)計說明。 An order tracking technique for the gear fault diagnosis using local meandecomposition methodJunsheng Cheng, Kang Zhang, Yu YangState Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, 410082, PR ChinaCollege of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, PR Chinaa r t i c l ei n f oa b s t r a c tArticle history:Received 17 November 2010Received in revised form 13 December 2011Accepted 30 April 2012Available online 28 May 2012Local mean decomposition (LMD) is a new self-adaptive timefrequency analysis method,which is particularly suitable for the processing of multi-component amplitude-modulatedand frequency-modulated (AMFM) signals. By using LMD, any complicated signal can bedecomposed into a number of product functions (PFs), each of which is the product of anenvelope signal and a purely frequency modulated signal from which physically meaningfulinstantaneous frequencies can be obtained. Theoretically, each PF is exactly a mono-componentAMFM signal. Therefore, the procedure of LMD can be regarded as the process of demodulation.While fault occurs in gear, the vibration signals would exactly present AMFM characteristics.Therefore, targeting the modulation feature of gear fault vibration signal in run-ups and run-downs and the fact that fault characteristics found in gear vibration signal could often be relatedto revolution of the shaft in the transient process, a gear fault diagnosis method in which ordertracking technique and local mean decomposition is put forward. The analysis results from thepractical gearbox vibration signal demonstrate that the proposed algorithm is effective in gearfault feature extraction. 2012 Elsevier Ltd. All rights reserved.Keywords:Order tracking techniqueLocal mean decompositionDemodulationGearFault diagnosis1. IntroductionGears are the important and frequently encountered components in the rotating machines that find widespread industrialapplications. Therefore, the corresponding gear fault diagnosis has been the subject of extensive research.The key step of gear fault diagnosis is the extraction of fault feature. On the one hand, the conventional gear fault diagnosismethods focus on examining the frequency spectrum analysis of vibration signal at a fixed rotation speed. Unfortunately, theinformation obtained thus is only partial because some faults maybe do not respond significantly at the fixed operation speed.Since faults commonly found in gear could often be related to revolution of the shaft, more comprehensive information may beacquired by measuring the gear vibration signal in the process of run-up and run-down 1. In addition, vibration signals derivedfrom gear in the transient process that are speed-dependent always display non-stationary feature. If frequency spectrum analysisis directly applied to the non-stationary vibration signal, frequency mixing would occur inevitably, which will bring undesirableeffect to the fault feature extraction. In past research, order-tracking technique, which normally exploits a vibration signalsupplemented with information of shaft speed of rotating machinery, has become one of the significant approaches for faultdiagnosis in rotating machinery 2,3. Essentially, order-tracking technique can transform a non-stationary signal in time domaininto stationary one in angular domain, which can highlight the vibration information related to rotation speed and restrain theunrelated information. Therefore, order tracking is a desirable method to extract gear fault feature in the process of run-up andrun-down.Mechanism and Machine Theory 55 (2012) 6776 Corresponding author at: State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, 410082, PR China.Tel.: +86 731 88664008; fax: +86 731 88711911.E-mail address: (J. Cheng).0094-114X/$ see front matter 2012 Elsevier Ltd. All rights reserved.doi:10.1016/j.mechmachtheory.2012.04.008Contents lists available at SciVerse ScienceDirectMechanism and Machine Theoryjournal homepage: the other hand, while faults occur in gears, the vibration signal picked up in run-up and run-down process always presentthe characteristics of amplitude-modulated and frequency-modulated (AMFM). In order to extract the modulation featureof gear fault vibration signals, demodulation analysis is one of the most popular methods 4,5. However, conventionaldemodulation approaches such as Hilbert transform demodulation and traditional envelope analysis have their own limitations6. These drawbacks include two aspects: (1) in practice most gear fault vibration signals are all multi-component AMFMsignals. For these signals, in conventional demodulation approaches, they are usually decomposed into single component AMFMsignals by band-pass filter and then demodulated to extract frequencies and amplitudes information. However, both the numberof the carrier frequency components and the magnitude of the carrier frequency are hard to be determined in practice, so theselection of central frequency of band-pass filter carries great subjectivity that would bring demodulation error and make itineffective to extract the characteristic of machinery fault vibration signal; (2) owing to the inevitable window effect of Hilberttransform, when Hilbert transform is used to extract the modulate information, the demodulation results present non-instantaneous response characteristic, that is, at the two ends of the modulated signal which has been demodulated as well as themiddle part with break would produce modulation again, which makes the amplitude get fluctuation in an exponentialattenuation way, and then the demodulation error would increase 7. In order to overcome the first drawback, an appropriatedecomposition method should be looked for to separate multi-component signal into a number of single component AMFMsignals before the envelope analysis. Since EMD (Empirical mode decomposition) could adaptively decompose a complicatedmulti-component signal into a sum of intrinsic mode functions (IMFs) whose instantaneous frequencies have physicalsignificance 8,9, order tracking method based on EMD has been widely used in the gear fault diagnosis 1013. However, therestill exist many deficiencies in EMD such as the end effects 14 and modes mixing 15 that are still underway. In addition, afterthe original signal is decomposed by EMD, the drawback produced by Hilbert transform (above mentioned) is inevitable whenIMF is performed envelope analysis by Hilbert transform. Moreover, sometimes the unexplainable negative instantaneousfrequency would appear when calculating instantaneous frequency by performing Hilbert transform to each IMF 16.Local mean decomposition (LMD) is a novel demodulation analysis method, which is particularly suitable for the processing ofmulti-component amplitude-modulated and frequency-modulated (AMFM) signals 16. By using LMD, any complicated signalcan be decomposed into a number of product functions (PFs), each of which is the product of an envelope signal (obtaineddirectly by the decomposition) from which instantaneous amplitude of the PF can be obtained and a purely frequency modulatedsignal from which a well-defined instantaneous frequency could be calculated. In essence, each PF is exactly a mono-componentAMFM signal. Therefore, the procedure of LMD could be, in fact, regarded as the process of demodulation. Modulationinformation can be extracted by performing spectrum analysis to the instantaneous amplitude (envelope signal, obtained directlyby the decomposition) of each PF component rather than by performing Hilbert transform to the PF components. Hence, whenLMD and EMD are applied to the demodulation analysis respectively, compared with EMD, the prominent advantage of LMD is toavoid the Hilbert transform. In addition, the LMD iteration process which uses smoothed local means and local magnitudes avoidsthe cubic spline approach used in EMD, which maybe bring the envelope errors and influence on the precision of theinstantaneous frequency and amplitude. Moreover, compared with EMD the end effect is not obvious in LMD approach because offaster algorithm speed and less iterative times 17.Based upon the above analysis, order-tracking analysis and the recent development of demodulation techniques, LMD, arecombined and applied to the gear fault diagnosis of various shaft speeds process. Firstly, order tracking technique is used totransform the gear vibration signals from time domain to angular domain. Secondly, decompose the re-sampling signal of angulardomain by LMD, thus s series PF components and corresponding instantaneous amplitudes and instantaneous frequencies can beobtained. Finally, spectrum analysis is carried out to the instantaneous amplitudes of the PF component containing dominant faultinformation. The analysis results from the experimental vibration signal show that the proposed method can extract fault featureof the gear effectively and classify working condition accurately.This paper is organized as follows. A theory of the LMD approach is given in Section 2. In Section 3 a gear fault diagnosisapproach in which order tracking technique and LMD are combined is put forward and the practice applications of proposedmethod are demonstrated. In addition, the comparison between LMD-based and EMD-based method is also given in Section 3.Finally, we offer the conclusion in Section 4.2. LMD analysis methodAs mentioned above, the nature of LMD is to demodulate AMFM signals. By using LMD a complicated signal can bedecomposed into a set of product functions, each of which is the product of an envelope signal and a purely frequency modulatedsignal. Furthermore, the completed timefrequency distribution of the original signal can be obtained. For any signal x(t), it can bedecomposed as follows 16:(1) Determine all local extrema niof the original signal x(t), and then the mean value miof two successive extrema niand ni+1can be calculated bymini ni121All mean value miof two successive extreme are connected by straight lines, and then local mean function m11(t)can be formed by using moving averaging to smooth the local means mi.68J. Cheng et al. / Mechanism and Machine Theory 55 (2012) 6776(2) A corresponding envelope estimate aiis given byainini1?22Similarly, the envelope estimate aiis smoothed in the same way and the corresponding envelope function a11(t) isformed.(3) The local mean function m11(t) is subtracted from the original signal x(t) and the resulting signal h11(t) is given byh11t x t m11t 3(4) h11(t) can be amplitude demodulated by dividing it by envelope function a11(t)s11t h11t =a11t 4Ideally, s11(t) is a purely frequency modulated signal, namely, the envelope function a12(t) of s11(t) should satisfya12(t)=1. If a12(t)1, then s11(t) is regarded as the original signal and the above procedure needs to be repeateduntil a purely frequency modulated signal s1n(t) that meets 1s1n(t)1 is derived. In other words, envelopefunction a1(n+1)(t) of the resulting s1n(t) should satisfy a1(n+1)(t)=1. Thereforeh11t x t m11t h12 s11t m12t h1nt s1 n1t m1nt 8:5in which,s11t h11t =a11t s12t h12t =a12t s1nt h1nt =a1nt 8:6where the objective is thatlimna1nt 17In practice, a variation can be determined in advance. If 1a1(n+1)(t)1+ and 1s1n(t)1, then iterativeprocess would be stopped.(5) Envelope signal a1(t), namely, instantaneous amplitude function, can be derived by multiplying together the successiveenvelope estimate functions that are acquired during the iterative process described above.a1t a11t a12t a1nt nq1a1qt 8where q is the times of the iterative process.(6) Multiplying envelope signal a1(t) by the purely frequency modulated signal s1n(t) the first product function PF1of theoriginal signal can be obtained.PF1t a1t s1nt 9PF1contains the highest frequency oscillations of the original signal. Meantime, it is a mono-component AMFMsignal, whose instantaneous amplitude is exactly the envelope signal a1(t) and instantaneous frequency is definedfrom the purely frequency modulated signal s1n(t) asf1t 12d arccos s1nt ?dt10(7) Subtract the first PF component PF1(t) from the original signal x(t) and we have a new signal u1(t), which becomes the neworiginal signaland the whole of the above procedure is repeated,i.e. up tok times,until ukbecomes monotonic functionu1t x t PF1t u2t u1t PF2t ukt uk1t PFkt 8:1169J. Cheng et al. / Mechanism and Machine Theory 55 (2012) 6776Thus, the original signal x(t) was decomposed into k-product and a monotonic function ukx t Xkp1PFpt ukt 12where p is the number of the product function.Furthermore, the corresponding complete timefrequency distribution could be obtained by assembling the instantaneousamplitude and instantaneous frequency of all PF components.3. The gear fault diagnosis method based on order tracking technique and LMD3.1. Order tracking analysis and the corresponding fault diagnosis methodOrder-tracking technique could transform a non-stationary signal in time domain into a stationary signal in angular domain byapplying equi-angular re-sampling to vibration signal with reference to shaft speed. Furthermore, order spectrum can be obtainedby using spectrum analysis to stationary signal in angular domain, thus the information related to rotation speed can behighlighted and the unrelated one could be restrained. Therefore, order-tracking is suitable for the vibration signal analysis ofrotation machine.There are three popular techniques for producing synchronously sampled data: a traditional hardware solution, computedorder tracking (COT) and order tracking based on estimation of instantaneous frequency 1820. The traditional hardwareapproach, which uses specialized hardware to dynamically adapt the sample rate, is only suitable for the case that rotating speedof shaft is relatively smooth, thus resulting to a high cost. The method of order tracking based on estimation of instantaneousfrequency has no need for specialized hardware and thus cost is relatively low, however, it has failed to analyze multiplecomponent signal. While in practice most gear fault vibration signals exactly present the characteristic of multi-component.Therefore, this technique has little practice significance. COT technique realized equi-angular re-sampling by software, thereforeit not only requires no specialized hardware, but also have no limitation for analysis signal that means it is more flexible and moreaccurate. Just for this reason, COT is introduced into the gear fault detection in this paper.The step of the gear fault diagnosis method based on order tracking technique and LMD can be listed as follows:(1) The vibration signals and a tachometer signal are asynchronously sampled, that is, they are sampled conventionally atequal time incrementst;(2) Calculate the time series ticorresponding to equi-angular increments by tachometer signals;(3) According to the time series ti, apply interpolation to the vibration signals, thus the synchronous sampling signal, namely,stationary signal in angular domain, can be obtained;(4) Use LMD to decompose the equi-angular re-sampling signal, thus s series PF components and corresponding instantaneousamplitudes and instantaneous frequencies can be acquired;(5) Apply spectrum analysis to the instantaneous amplitude of each PF component, and then we have the order spectrum.3.2. ApplicationSince the gear fault vibration signal in run-up and run-down process are always multiple component AMFM signals and faultfeature frequency would vary with rotation speed, the fault diagnosis method in which order tracking technique and LMD arecombined would be suitable for gear fault detection.To verify the effectiveness of the proposed method, the fault diagnosis method based on order tracking technique and LMDwas applied to the experimental gear vibration signals analysis. An experiment has been carried out on the rotating machinerytest rig that is used for modeling different gear faults 21. Here we consider three working conditions that are gear with normalcondition, with cracked tooth and with broken tooth. Standard gears with teeth number z=55 and z=75 are used on input andoutput shafts respectively, in which the crack fault is introduced into the gear on the input shaft by cutting slot with laser in theroot of tooth, and the width of the slot is 0.15 mm, as well as its depth is 0.3 mm. Therefore, the mesh order is xm=55 and thefault feature order is xc=1. Figs. 1 and 2 give the rotation speed signal r(t) picked up by a tachometer and vibration accelerationsignal s(t) of the gear with crack fault collected by a piezoelectric acceleration sensor respectively, in which the sample frequencyis 8192 Hz and total sample time is 20 s, and from which we know the speed of input shaft increased gradually from 150 rpm to1410 rpm, then decreased to 820 rpm. Meantime, the amplitude of vibration acceleration signal accordingly changed, from whicha section of signal s1(t) of 5 s7 s in the run-up progress is intercepted for further analysis. Fig. 3 gives the spectrum of s1(t) byapplying spectrum analysis directly to vibration signal. For the rotation speed changes with time, the frequency mixing arises.Therefore, it is impossible to find meshing frequency and fault feature frequency in Fig. 3. As a result, actual gear workingcondition cannot be identified. Replace direct spectrum analysis by the order tracking method. Firstly, assume sample point perrotation is 400, namely, the maximum analysis order is 200. Secondly, angular domain signal j1() shown in Fig. 4 can be obtainedby performing order re-sampling to s1(t), in which horizontal ordinate has changed from time to radian. Thirdly, thecorresponding order spectrum of j1() can be calculated that is illustrated in Fig. 5, from which we can find obvious spectral peak70J. Cheng et al. / Mechanism and Machine Theory 55 (2012) 6776values at order O=55 and O=110 corresponding to gear meshing order and the double. Thus it means that frequency aliasingphenomenon has been eliminated to a large degree. However, j1() is still a multiple component MAMF signal. Therefore, sidefrequency band reflecting fault feature frequency is indistinct. To extract fault characteristic effectively, apply LMD to j1(), thusseven PF components and a residue can be obtained shown in Fig. 6, which means LMD is a demodulation progress. Therefore, it ispossible to extract gear fault feature by utilizing spectrum analysis to the instantaneous amplitude of PF component containingdominant fault information. By analysis, we know that the main failure information is included in the first PF component.Therefore, Figs. 7 and 8 give instantaneous amplitude a1() of the first PF component PF1() and the corresponding orderspectrum of a1(), from which it is clear that there are distinct spectral peak value at the 1st order (O=1) corresponding to gearfault feature order xc, which accords with the actual working condition of the gear.Figs. 9 and 10 show the rotation speed signal n(t) and the time domain waveform of vibration acceleration signal s(t) of thegear with broken tooth respectively, in which the sample rate is 8192 Hz and total sample time is 20 s. The broken tooth fault isintroduced into the gear on the input shaft by cutting slot with laser in the root of tooth. Firstly, a section of signal s1(t) of 5 s7 sin the run-up progress is intercepted for further analysis; secondly, assume sample point per rotation is 400; thirdly, angulardomain signal j1() shown in Fig. 11 can be obtained by performing order re-sampling to s1(t); fourthly, apply LMD to j1();finally, the corresponding order spectrum shown in Fig. 12 of instantaneous amplitude of the first PF component PF1() can beacquired, from which it is clear that there are distinct spectral peak value (it is bigger than that in Fig. 8) at the 1st order (O=1)corresponding to gear fault feature order xc, which accords with the actual working condition of th