机械毕业设计外文翻译 - 图文

毕业设计（论文）外文资料翻译

学院： 机械工程学院 专业： 机械设计制造及其自动化 班级： 机113班 姓名： 学号： 2011307310 外文出处： Availabie online at www.sciencedirect Ultransonics 42(2004) 169-172 附 件：1、外文原文；2、外文资料翻译译文。

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附件1：

Friction generated ultrasound from geotechnical materials Abstract

Drilling is a process involved with product manufacturing and for civil engineers, site preparation. The usual requirement is for efficient material removal. In this study, the friction pair interaction generated by a drilling process provides ultrasound information related to parameters for the geotechnical material being drilled, where the drill bit has non-degrading ultrasonic characteristics and no essential requirement for material removal. This study has considered monitoring the ultrasonic signal generated by drilling process, with a view to characterising the parameters of the geotechnical material being drilled and provides a novel method to identify or characterise ground structures. Drilling of geotechnical material systems, typically involve the interaction of a rotating probe and a granular composite medium. The applied load and angular velocity are measured to determine their relevance to the ultrasonic signal. Samples of granular materials have been graded into controlled grain size ranges. Attention has been focused on determining the effects on the ultrasound signal of grain size, bulk density and the water content of the granular material. A comparison between the various granular samples of the different grain sizes, density, water content and the associated ultrasonic signal has been done. The effect of each variable, and existing theory for these effects is commented upon. The broad aim of this research is to evaluate ultrasonic monitoring of drilling and assess its potential for real-time geotechnical ground condition monitoring applications and offer it as an alternative to existing methods. _ 2004 Published by Elsevier B.V.

1. Introduction

The ultrasound generated from a solid–solid friction pair has been the main focus of research concerning friction-generated ultrasound, mainly associated with rotating and reciprocating machines. A frictional process developed during relative movement between contacting materials has an inherent level of wear that eventually would result in failure. Monitoring the ultrasonic signal generated from machinery has become an alternative condition-monitoring tool, as the generated signal contains information related to the microcondition of the friction pair. It is possible to detect when components of a machine are becoming worn and a thus reduce the risk of catastrophic failure leading to production down time. Holroyd and Randall [1]

discussed the sensitivity of using acoustic emission (AE) for detecting changes in lubrication, overloading, wear and review a number of different techniques used to analysethe acoustic signature. Further methodologies for analysing the friction generated acoustic signatures were discussed by Bukkapatnam et al. [2] and provide a novel analysis technique based on chaos theory, wavelets and neural networks. Much of the research concerning condition monitoring focuses on the changes in the signal due to wear, but some research have also focused on the parameters associated with the generated acoustic signal.Work by Diei [3] monitored the acoustic emission generated by tool wear during face milling and proposed a power function relationship between the AERMS voltage and the rate of frictional energy dissipationgiven by AERMS ekgssAaV Tm=2 e1T where k and m are constants that depend on the AE measuring system and the material properties of the friction pair, g is a function of surface roughness and elastic properties of the friction pair, ss is the shear strength of the interfacial material, Aa is the visible area of contact and V is the sliding velocity. The parameters g and Aa essentially define the real area of contact andtherefore, the AERMS is a function of the real area of contact, the shear strength and the sliding velocity. Results obtained by Diei’s work also indicated a linear relationship between the AERMS and the sliding velocity. Jiaa and Dornfield [4] monitored the AE generated by a pin on disk experiment, highlighting that the AE is caused by impulsive shock due to asperity collisions and micro-vibrations excited by stick–slip phenomena. The research shows that the AERMS increases with load while a linear relationship exists between the relative surface velocity and the AERMS. Sarychev and Shchavelin [5] describe the frictional process and the generated acoustic emission associated with it. Two general rules were established relating the rate of counting the acoustic pulses (count rate) to the sliding speed of the friction pair and the applied load.

The general rule for the dependence of the count rate N_ on the sliding velocity is in the form: N_ A t BvX e2T where A and B are constants and X P1. A similar relationship also applies for the dependence of the load on the count rate, but the exponent X 61. A further relationship was expressed relating the AE activity to the regime of friction in elastic contact: N_ a k N0:71h0:71A0:71 c r0:90R1:60 a V e3T where N is the normal load, h the generalised elastic modulus, Ac the counter area of contact, r the surface asperity tip radius, Ra is the surface roughness and k is a coefficient of proportionality. Further work by Baranov [6] produced two models

relating the frictional parameters of the friction pair to the acoustic parameters; count rate and acoustic energy. The model for the count rate is based on the assumption that the rate of counting acoustic pulses is directly proportional to the number of contact points formed per unit time. Work by Henrique et al. [7] studied particle collisions down an inclined slope and the number of acoustic events were used to monitor the number of collisions (contacts) generated when a ball was rolled down the slope. The model for the acoustic energy relates the mechanical potential energy generated during the elastic deformation of a contacting asperity to the amplitude distribution of the acoustic signal. The energy model does not take into consideration the effects of wear and is based on the AE generated due to elastic contact.

Current studies in friction-generated acoustics have shown that the acoustic signals contain information relating to the material parameters of the friction pair. The work in this study uses the acoustic signal as a tool to characterise the material properties of the friction pair. The idea for this study originates from a study by Hill [8] for Scientifics, when it became apparent that monitoring the ultrasound generated by a drilling process process had potential for ground condition monitoring. The overall aim of this work is to develop a method of characterising geotechnical materials using a typical drilling process and monitoring the ultrasound generated due to the interaction between the drill tip and the geotechnical material.

2. Experimental design

A simplified drilling arrangement has been constructed where a rotating probe is used to maximise the friction at the probe-tip–granular contact. The probe string is designed, using a suitable coupling device, so that the ultrasonic signal is transmitted from the probe tip to a stationary piezoelectric sensor. The signal is amplified by 60 dB and filtered between 250 and 500

kHz. The captured signal is therefore in the mid-ultrasonic range and relates to the transducer monitoring frequency used. A schematic diagram of the experimental arrangement can be seen in Fig. 1. The probe rotates, while being submerged in a granular medium of controlled particle size, initial density and water content. The feed rate and angular velocity were set to a constant value and the applied load, count rate and ultrasonic energy were simultaneously monitored. The effects of the particle size, density and water content on two ultrasonic parameters (count rate and energy) have been investigated and the system aims to be a future option for ground condition monitoring. 3. Results

The effect of load on the count rate can be seen in Fig. 2a. The signal values on the left of the figure correspond to the probe tip not being in contact with the granular medium. When the probe is pushed into the granular material the load increases. The data highlights a stabilizatio(reduction) in the count rate and is referred to as the ‘‘characteristic count rate’’ for a particular friction pair. The stabilisation of the count rate means that no more oscillations are being produced due to an increase in the load and therefore the signal amplitude is only subject to amplitude increase. Different grades of particulate material have been used and the characteristic count rate monitored. The results indicate that a lower characteristic count rate occurs as the average particle size is increased. Eight samples of sand were used and the characteristic count rate is compared with the particle size in Fig. 2b. Larger particle sizes produce fewer contacts and therefore the results agree with the assumption

stated by Baranov et al. [6] that, the rate of counting is proportional to the number of contacts formed per unit time. The results in Fig. 2c reveal that the water content has little effect on the characteristic count rate. Four ranges of grain size have been used and the count rate is plotted against the mass percentage water content. There is a small variation in the count rate but the separation in the signals generated by the different particle sizes still exist. Results have revealed that the count rate value does not significantly change due to the addition of water and that the count ratesignal is mainly dependent on the number of contacts formed. Therefore, regardless of the water content of the sand it is possible to obtain an approximate evaluation of the average particle size.

The ultrasonic signal energy appears to be sensitive to a number of parameters including the particle size, water content, density and mineralogy. Fig. 3a shows the ultrasonic energy signal plotted against the applied force for two different initial dry densities (compacted and loose). Results indicate that the energy varies linearly with the applied load and the gradient increases with a reduction in the initial density. The

effect of varying the density is more apparent when using smaller grain sizes. A change in the density using smaller particulate material will produce a larger affect on the number of probe– granular contacts generated within the apparent contact area. Lower particulate densities produce fewer contacts and therefore the pressure due to the applied force is increased and may account for an increase in the average energy per oscillation as a function of the applied force. It is expected that an increase in the particulate size would also produce an increase in the acoustic energy as a result of higher contact pressures. Fig. 3b shows the change in the average energy per oscillation due to the applied force against the average particle diameter. Results indicate that there is no unique relationship between this ultrasonic energy parameter and the particle size, with a peak occurring at 512 lm.

The effect of increasing the water content of the granular sample causes the sand to become acoustically significant

drop

quieter in

(a signal

amplitude). Although the sand becomes quieter, the rate of

change of the ultrasonic energy due to the applied force is not affected by varying the level of water content in a wet sample but there is a noticeable difference in the gradient when comparing a dry sample with a wet sample. 4. Conclusions

Results have shown that when probing into granular materials, using a constant sliding velocity the count rate becomes stable (characteristic count rate). The characteristic count rate is affected by a change in the number probe–granular contacts and therefore provides a method for characterising the particle size. The water content of a granular sample has little effect on the characteristic count rate and data agrees with the assumption stated by Baranov et al. [6] that the count rate is proportional to the number of contacts formed per unit time. However, the data does not agree with the general rule suggested by Sarychev and Shchavelin [5], as the characteristic count rate does not depend on the applied force. Results provide

positive evidence that monitoring the characteristic count rate has potential as a tool for identifying the layers of different particle size in ground structures regardless of the moisture content.

The ultrasonic energy signal is sensitive to a variety of parameters including the load, sliding velocity, particle size, density, water content and mineralogy. Results have indicated that the contact pressure, which is affected by altering the density and particle size, affects the acoustic energy signal. However, a continuous increase in the ultrasonic energy due to larger particle sizes, which was expected, did not occur. It is possible that larger particles produce larger particle-probe contact areas thus reducing the contact pressure at a single contact spot but further work is needed for this to be established. It is clear that the ultrasonic energy contains information relating to the parameters of the friction pair but further investigation is required to fully understand the contribution of each parameter associated with the generated acoustic signal.

References

[1] T.J. Holroyd, N. Randall, Use of acoustic emission for machine condition monitoring, Condition Monitoring 35 (2) (1993) 75–79.

[2] S.T.S. Bukkapatnam, S.R.T. Kumara, A. Lakhtakia, Analysis of acoustic emission signals in machining, ASME Journal of Manufacturing Science and Engineering (1999) 183–207.

[3] E.N. Diei, Acoustic emission sensing of tool wear in face milling, Journal of Engineering for Industry 109 (1987) 234–240.

[4] C.L. Jiaa, D.A. Dornfield, Experimental studies of sliding friction and wear via acoustic emission signal analysis, Wear 139 (1990) 403–424.

[5] G.A. Sarychev, V.M. Shchavelin, Acoustic emission method for research and control of friction pairs, Tribology International 24 (1) (1991) 11–16.

[6] V.M. Baranov, E.M. Kudryavtsev, G.A. Sarychev, Calculation of the parameters of acoustic emission when there is external friction between solids, Russian Journal of Non-Destructive Testing 8 (1995) 569–577.

[7] C. Henrique, M.A. Aguirre, A. Calvo, I. Ippolito, D. Bideau, Experimental acoustic technique in granular flows, Powder Technology94 (1997) 85–89. [8] R. Hill, Confidential consultancy Report, Scientifics, 1997.

附件2：

岩土材料的摩擦声波

摘要

钻井作业涉及到设备生产，对于工程师来说，还包括地址的选择。钻井通常要求高效地去除材料。在这项研究中，钻井过程中摩擦副的相互作用提供了被钻削岩土材料相关参数的超声信息，在这些信息中钻头具有非降解的超声特性，对于材料去除没有基本的要求。这项研究认为监测钻井过程中产生的超声波信号，为表征岩土材料钻削参数提供了新的观点，并提供了一种识别或表征地面结构的新方法。岩土材料系统的钻削，通常涉及一个旋转探头和颗粒复合介质的相互作用。测量旋转探头的载荷和角速度可用来确定它们和超声信号的相关性。颗粒材料的样本已经把粒径控制在一定范围内。精力主要集中在确定晶粒尺寸、堆积密度和颗粒材料的含水量对超声信号的影响。将不同粒径、密度、水分含量和相关超声波信号的颗粒样本进行比较，解释每个变量的影响和有关这种影响的现有理论。这项研究一般的目的是评估钻井的超声监测，并估计其在岩土地表实时状态监测中的应用潜力，用它来代替现有的一些方法。 1.引言

固体–固体摩擦副产生的超声波是摩擦产生超声波研究的重点，主要涉及旋转和往复运动的机械。相互接触的材料相对运动过程中的摩擦产生固有的磨损，最终会导致工作故障。监测机械产生的超声波信号已成为一种不可替代的机器状态监测方法，因为超声波信号包含了与摩擦副的微观环境相关的信息。当机器零件发生磨损时，用这种方法来查明故障是可行的，因而降低了因灾难性故障导致生产停机所带来的风险。Holroyd和 Randall [ 1 ]论述了利用声音辐射技术（AE）检测润滑、超载、磨损变化的灵敏度问题，并查阅了许多其他用于分析声学特征的技术。Bukkapatnam等人[ 2 ]论述了用于分析摩擦声信号更先进的方法，并提出了一种基于混沌理论、小波和神经网络的新的分析技术。大多数有关状态监测

的研究更关注由磨损引起的声信号变化，而一些研究也已经注意到与产生声信号相关的参数。Diei [3]监测了表面磨削过程中刀具磨损所产生的声音辐射，提出了AERMS电压和摩擦能量耗散率之间的幂函数关系 ekgssAaV Tm=2 e1T 。其中k和m取决于声音辐射测量系统和摩擦副材料性能常数；g是摩擦副的表面粗糙度和弹性度的函数；ss是界面材料的剪切强度；Aa是有效接触面积，V是滑动速率。参数g和Aa基本决定了有效接触面积，因此AERMS是有效接触面积、抗剪强度和滑动速率的函数。Diei研究的结果也表明AERMS和滑动速率线性相关。Jiaa and Dornfield [ 4 ]监测了大头针在磁盘上所产生的声音辐射，表明声音辐射是由于微凸体碰撞和粘滑现象所激发的微振动产生脉冲冲击引起的。 这项研究表明当表面相对速度和AERMS之间存在线性关系时，AERMS随负荷增加而增加。Sarychev and Shchavelin [5]描述了摩擦过程及其产生的声音辐射，建立了两条与摩擦副滑动速度及因加载产生的声脉冲计数速率（计数率）相关的基本原则。计数率N_对滑动速率依赖性的基本原则的形式是： N_ A t BvX e2T 。其中，A和B是常数，X取P1。同样的关系也适用于载荷对于计数率的依赖性，只是指数X取61。进一步把声音辐射活跃度与弹性接触产生摩擦的机理联系起来，其关系表示为：N_ a k N0:71h0:71A0:71 c r0:90R1:60 a V e3T 。其中，N是正常负荷量，h是广义弹性模量，Ac表示非接触区的面积，r为表面粗糙度的尖端半径，Ra是表面粗糙度，k为比例系数。Baranov[6] 做了进一步的研究，给出了两种模型：把摩擦副的参数与声音参数联系起来；把计数率与声音能量参数联系起来。计数率模型是基于声音脉冲计数率与单位时间内所形成接触点的数目成正比的假设。 Henrique等人[ 7 ]研究了粒子沿着斜坡向下碰撞，及用于检测一个球滚下斜坡时产生碰撞（接触）次数的声音发生次数。声音能量模型把一个接触的微凸体弹性变形过程中产生的机械势能与声信号的振幅分布联系起来。这种声音能量模型没有考虑磨损的影响，而且是基于弹性接触产生的声音辐射。

目前摩擦声学研究已经表明声音信号包含了与摩擦副材料参数相关的信息。这项研究工作用声信号作为一种表征摩擦副材料特性的工具。当监测钻井过程中产生的超声波对地面状态监测的作用变得明显的时候，这种思想在Hill[ 8 ]为Scientifics所做的一项研究中产生了。这项工作的总体目标是研究一种利用典型钻井过程并监测钻头和岩土材料相互作用产生的超声波来描述岩土材料特性的方法。 2.实验设计

一个简单的钻井装置具有一个使探针–颗粒接触面积最大化的旋转探头。使用合适的耦合

装置设计探头串，使超声波信号从探头传输到一个固定的压电传感器。该信号被放大了60分贝并过滤掉250和500千赫之间的信号。因此所得到的信号在中超声波的范围内并与所使用传感器的监测频率有关。实验装置示意图如图1所示。探头伸入具有一定粒径、初始密度和含水量的颗粒介质中旋转。探头的进给速度和角速度设置为一定值，并同时监测所施加的载荷、计数率和超声波能量。粒径、密度和含水量对两种超声参数（计数率和能量）的影响被进行了研究，这种研究的目的是为未来地面状态监测提供一种选择。 3.实验结果

载荷对于计数率的影响如图2a所示。位于图左侧的信号数值对应不与颗粒介质接触的探针。当探头推入颗粒材料时，负载随之增加。对于计数率模型，所得数据要求稳定可靠（原始），并将之称为一个特定摩擦副的“特征计数率”。计数率的稳定意味着负载的增加不会产生更多的震荡，因此信号幅度只受振幅增加的影响。实验中使用了不同等级的颗粒材料，并且其特征计数率也已被监测。 研究结果表明，随着平均粒径的增大特征计数率会降低。在图2b中，对八份沙子样本进行了特征计数率与粒径的比较。粒径越大，接触面积就越小。因此实验结果印证了Baranov等人[ 6 ]提出的计数率与单位时间内接触次数成正比的假设。在图2c中，实验结果表明含水量对特征计数率影响不大。使用四种不同尺寸的晶粒，

画出计数率与质量含水率的关系曲线。尽管计数率发生很小的变化，但是不同粒径产生的信号分离仍然存在。实验结果表明，增加水含量不会引起计数率地显著变化，计数率信号主要取决于产生接触的次数。因此，无论沙子含水量多少，都可以获得其平均粒度的近似计算。

超声波信号的能量似乎对包括颗粒大小、含水量、密度和矿物质含量在内的一些参数很敏感。图3a显示出超声波能量信号与施加在两份不同初始干密度（松散与密实）样本上的力的关系曲线。实验结果表明，超声波信号的能量与施加的荷载线性相关，相关线的斜率随样本初始密度的降低而增加。当使用较小的晶粒尺寸时，密度变化对超声波信号能量的影响更为显著。使用粒径更小的颗粒材料时，密度的变化将对表面接触区中探针–颗粒接触次数产生更大的影响。较低的颗粒密度产生更少的接触，因而荷载产生的压力增大，并可能使荷载的函数——平均每振荡的能量增加。预计由于较高的接触压力颗粒尺寸的增加也会使超声波能量增加。图3b显示了由于对平均粒径施加力而引起平均每振荡能量的变化。结果表明在粒径达到512 lm时超声波能量参数和粒径之间没有特殊的关系。

增加颗粒样本的含水量导致沙子变得更安静（声信号幅值显著下降）。虽然沙子变得更安静，在潮湿的沙子

样本中因施力产生的超声波能量的变化率不受水含量变化的影响，但是当把干燥的样本与潮湿的样本进行比较时，它们在声波梯度上仍有明显的差异。 4.结论

实验结果表明，用一个恒定的滑动速率探查颗粒材料时，计数率趋于稳定（特征计数率）。特征计数率受到探针–颗粒接触次数的影响，从而提供了一种用于表征颗粒粒径的方法。颗粒样本的含水量对特征计数率几乎无影响。而且实验所得数据印证了Baranov等人[ 6 ]所提出的计数率与单位时间内接触次数成正比的假设。然而，实验所得数据不符合Sarychev和shchavelin [ 5 ]提出的一般规律，因为特征计数率不依赖于所施加的力。实验结果提供了有力的证据来表明监测特征计数率具有实现不依赖水分含量而识别地面结构不同粒度层的潜力。

超声波能量信号对于包括负载、滑动速率、粒径、密度、含水量和矿物质含量在内的多种参数很敏感。实验结果表明受密度和粒径变化影响的接触压力会影响声波能量信号。然而，在意料之中，由于更大的颗粒尺寸而引起超声波能量持续地增加不会发生。较大的颗粒产生较大的粒子-探头接触面积从而减小单一接触点的接触压力是可能的，但需进一步研究来支持这种观点。很明显，超声波能

量包含了与摩擦副参数相关的信息，但是为了完全了解与产生声音信号相关的每个参数的分布，还需要做进一步的研究。

参考文献

[1] T.J. Holroyd, N. Randall, Use of acoustic emission for machine condition monitoring, Condition Monitoring 35 (2) (1993) 75–79. [2] S.T.S. Bukkapatnam, S.R.T. Kumara, A. Lakhtakia, Analysis of acoustic emission signals in machining, ASME Journal of Manufacturing Science and Engineering (1999) 183–207.

[3] E.N. Diei, Acoustic emission sensing of tool wear in face milling, Journal of Engineering for Industry 109 (1987) 234–240.

[4] C.L. Jiaa, D.A. Dornfield, Experimental studies of sliding friction and wear via acoustic emission signal analysis, Wear 139 (1990) 403–424.

[5] G.A. Sarychev, V.M. Shchavelin, Acoustic emission method for research and control of friction pairs, Tribology International 24 (1) (1991) 11–16.

[6] V.M. Baranov, E.M. Kudryavtsev, G.A. Sarychev, Calculation of the parameters of acoustic emission when there is external friction between solids, Russian Journal of Non-Destructive Testing 8 (1995) 569–577. [7] C. Henrique, M.A. Aguirre, A. Calvo, I. Ippolito, D. Bideau, Experimental acoustic technique in granular flows, Powder Technology94 (1997) 85–89.

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