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1、济南大学毕业论文外文资料翻译毕业论文外文资料翻译题 目 平面磨削中形位误差的改进型离散系统模型学 院 济南大学机械工程学院 专 业 机械工程及自动化 班 级 机自 0807 学 生 鲁帅帅 学 号 20080403147 指导教师 昃向博 二一二 年 月 日- 13 -Journal of Materials Processing Technology,210(2010)1794-1804平面磨削中形位误差的改进型离散系统模型Y.Gao1, X.Huang1, Y.Zhang1香港科技大学机械工程系Keywords:Surface grinding; Partial removal; In-p
2、rocess sensing; Precision control; Model; Form errorAbstract:Grinding remains as one of few choices being able to machine very hard materials to deliver ultra high precision at high material removal rate for efciency. Effective models are needed for precision control of the machining process. So far
3、, few studies on form error prediction have been reported. Machining usually begins with partial removal of workpiece surface. Without in-process sensing, system parameters could not be accurately determined nor surface form information thus preventing us from modeling for precision control. In this
4、 study, an improved discrete system model and an in-process sensing technique have been proposed to address the partial removal and precision control problems. Models for partial removal, full removal, and sparking out conditions have been established. Form error assessment in the partial removal st
5、age has been investigated. It is found that the grinding constant is able to reect changes in machining conditions and is able to represent machining capability. A larger grinding constant will mean a reduced size reduction. Further studies of the grinding constant are necessary. For the accurate es
6、timation of the grinding constant, two approaches are proposed. The iterative approach was found more suitable and convergent. The proposed models and in-process sensing technique were validated through experimental testing in terms of workpiece surface form prole yn(x,z0), average size reductioncn,
7、 surface form error Epvn and normal grinding force Fnn. Through detailed examination and comparative studies, the proposed models and in-process sensing technique offered signicant improvements ranging from approximately 16.9% to 23%, compared with the existing models. Except the grinding force, whi
8、ch was indirectly measured through a voltage measurement approach, the overall relative errors between the theoretical results and the experimental results under full removal conditions were found ranged from 2.08% to 6.87%, indicating the improved precision prediction capabilities of the proposed s
9、ystem model. The experimental results can be used as a set of references for further studies to offer performance assessment, precision prediction, process planning, and process condition monitoring for this important precision machining process.1. Introduction1.1. Model for precision controlGrindin
10、g is an abrasive precision machining process which remains as one of few choices being able to machine very hard materials to deliver ultra high precision at high material removal rate for efciency. Process of the kind is widely used to achieve high accuracy for high quality mechanical, electrical,
11、and optical parts (Karpuschewski and Inasaki, 2006). Surface grinding is one for precision machining of surfaces. To achieve higher accuracy for quality control, it is essential to develop effective models to realize precision control of the machining process.For grinding process modeling, models of
12、 multiple aspects (Baasz and Krlikowski, 2007), such as model of grain (Horng, 2008; Mamalis et al., 2001), model of grinding wheel topography (Bigerelle et al., 2005; Zhou and Xi, 2002), model of heat trans-fer (Liao et al., 2000), model of process kinematics (Weck et al.,2001; Zhang et al., 2005),
13、 model of chip formation (Gopal and Rao,2004; Hecker et al., 2007), model of force (Hekman and Liang, 1999;Jenkins and Kurfess, 1999; Tang et al., 2008), and model of power (Nandia et al., 2004), have been examined.The grain or material removal model (Horng, 2008) was based on surface asperity conta
14、ct mechanics. The elasticplastic effects in the wear mechanism were considered to be related to the density of abrasive grains. Mamalis et al. (2001) proposed a model for interaction between hard polycrystalline materials and wheel grain during grinding. Worn surfaces of grinding wheel may be modele
15、d using fractal functions (Bigerelle et al., 2005). A roughness prediction model for wheel topography, wear, and grinding kinematics was established by Zhou and Xi (2002). The thermal model by Liao et al. (2000) involved a thermal effect of grain and workpiece interface and a shear plane between wor
16、kpiece and chip. The temperature of the workpiece surface in the grinding zone could be predicted. A dynamic behavior model for the cylindrical traverse grindingprocess in the time domain was presented by Weck et al. (2001). A nonlinear dynamic model to investigate the dynamic characteristics of the
17、 grinding process was proposed by Zhang et al. (2005). A chip thickness model by Gopal and Rao (2004) was developed for assessment of silicon carbide grinding by incorporating the modulus of elasticity of the grinding wheel. Based on the statistical distribution of undeformed chip thickness, predict
18、ive models for grinding force and power were proposed by Hecker et al. (2007).To control the depth of cut, a grinding force based model may be used (Hekman and Liang, 1999). A real time grinding force model (Jenkins and Kurfess, 1999) was used to control the grinding normal force. The sliding force
19、model for surface grinding (Tang et al.,2008) involved process parameter effects on friction coefcient. For power requirement and surface nish prediction, a GA-fuzzy model may be used (Nandia et al., 2004). 1.2. Form errorIt can be seen that the models previously examined are mainly related to rough
20、ness, power, heat, chip thickness, and dynamic characteristics in a grinding process. Form error, as one of the most critical quality elements of a workpiece among size, form and roughness (Whitehouse, 2002), remains a difcult issue. Very few existing studies on form error prediction for surface gri
21、nding can be found. 1.3. Partial removal problemFor a typical surface grinding process, due to initial surface prole error and misalignment involved in workpiece and workpiece mounting, machining usually begins with partial removal of workpiece surface (Fig. 1). If this condition is not examined as
22、in our previous model (Huang and Gao, 2010), we will experience a signicant amount of error in prediction of form accuracy in the machining process (Huang and Gao, 2010). As such, our ability for precision control (Gao and Jones, 1993), which is very desirable (Ludwick et al., 1999; Kim and Trumper,
23、 1998), could be reduced.1.4. In-process sensing problemIn our previous model (Huang and Gao, 2010), in-process measurement developed by Gao et al. (2009, 2010) was not available and the initial workpiece prole y0(x,z) was assumed to be zero.Because of this, we could not accurately determine system
24、parameters and we could not acquire accurate surface form information, which could prevent us from modeling the machining process for precision control (Gao and Jones, 1993; Huang and Gao, 2010), which is much desired (Ludwick et al., 1999; Kim and Trumper, 1998).In our previous test (Huang and Gao,
25、 2010), the machining process was not fully modeled. The maximum modeling error was up to 19.9% (Huang and Gao, 2010).1.5. Improved discrete system model and in-process sensing techniqueIn order to solve the above problems to achieve higher modeling accuracy for precision control (Gao and Jones, 199
26、3; Huang and Gao, 2010), a new in-process surface form prole measurementprototype system (Gao et al., 2009, 2010) was developed. After theuse of the in-process form error sensor, accurate parameter estimation and the condition of partial removal will be included in animproved discrete system model,
27、which is proposed for the surface grinding process. The improved discrete system model will be examined.The proposed new model and in-process sensing technique will be useful under both partial and full removal conditions. It can be used to predict workpiece form error throughout a grinding process.
28、 In addition, the improved model will allow advanced control to achieve high efciency in ultra precision and nanoprecision machining.2. In-process form error measurementFor accurate parameter estimation and identication of partial removal condition, in-process measurement of form error prole yn(x,z)
29、 (Fig. 1) will be used to enhance modeling accuracy. Without the in-process form error measurement (Figs. 1 and 2),the workpiece has to be removed from the machining position to measure ofine to obtain the initial workpiece prole y0(x,z). A signicant amount of error was caused as the workpiece has t
30、o be mounted again after the ofine measurement (Huang and Gao,2010). This is particularly true for nonferrous materials such as Al,Cu, and Si which are commonly used where magnetic chuck will no longer be functional.2.1. PrototypeTo avoid the scratch problem in the contact approach (Whitehouse, 2002
31、), a new optical in-process surface form measurement prototype system has been developed and has been used on a real surface grinding machine (Fig. 2) (Gao et al., 2009, 2010).The measurement system is mainly made up of a triangular laser sensor, applicator and air piece. The type of the laser senso
32、r is Cyber Optics DRS300. It has a resolution of 50 nm and is used to measure workpiece surface form prole yn(x,z) (Figs. 1 and 2). The applicator is for reducing the amount of coolant near the measurement region (Fig. 2). This will be benecial for establishing a transparent window for optical measu
33、rement. The air piece is to allow an air beam to be ejected and then impinged on the workpiece surface to remove coolant. The system is the rst one of the type for workpiece form prole measurement for which the coolant problem that prevents use of high precision optical approach was solved (Gao et a
34、l., 2010).2.2. Opaque barrier and vibrationDue to two key problems, which are opaque barrier and vibration (Gao et al., 2010), the study of in-process form error optical measurement for precision machining has been a hard topic requiring intensive and extensive study. Another problem is the inherenc
35、e access problem (Marinescu et al., 2004). So far very few existing research works can be found, although there are many existing optical approaches for roughness measurement since 1980s, all not being able to deal with the two key problems, in particular, the opaque barrier problem. The proposed ne
36、w measurement system (Gao et al., 2010) is able to deal with the two key problems. This allows high precision in-process surface form prole measurement without the scratch problem (Whitehouse, 2002) on a real grinding machine (Gao et al.,2010) (Fig. 2).Further studies are planned for developing a be
37、tter system ready for wide industrial use (Gao et al., 2010). Research of the kind is much needed prior to industrial application. Intensive characteristics and optimization study should be good in order to deliver a technology ready for real industrial use.It is noted that many lower precision and
38、contact stylus type(Whitehouse, 2002) in-process sensors have been used in industry(Gao, 1998) for many years. Using high precision and non-contact optical sensors (Gao et al., 2010) is surely a good direction to move forward.2.3. RepeatabilityThe measured prole and corresponding position informatio
39、n yn(x,z) are both collected by a PC (Gao et al., 2009, 2010). Techniques such as damping and moving average have been applied in this system to reduce measurement error (Gao et al.,2009, 2010).A measurement error of 0.3m was achieved in the tests on a grinding machine (Gao et al., 2009, 2010).3. Mo
40、del of partial removal conditionIn surface grinding, due to initial surface prole error and misalignment involved in workpiece and workpiece mounting, machining usually begins with partial removal of workpiece surface (Fig. 1). This is a common problem in surface grinding. The problem was experience
41、d in our previous modeling study (Huang and Gao, 2010) and signicant amount of error was found in prediction of form accuracy in the machining process (Huang and Gao,2010).3.1. Partial removalPartial removal means material removal occurs only at part of a grinding path along the x direction (Fig. 1)
42、. For the remaining part of the grinding path, there is no engagement between wheel and workpiece. After a number of grinding passes, partial removal is to be followed by a stage of full removal, where the grinding wheel and the workpiece are engaged through out a grinding pass.3.2. Partial removal
43、conditionTo study the partial removal stage, a new model of size reduction condition is proposed.A general size reduction model cn(x,z) has been established(Huang and Gao, 2010) as:where T(x,z) is the grinding constant (Huang and Gao, 2010) andand kw is the grinding force constant (Huang and Gao, 20
44、10).3.3. Form error assessment in the partial removal stage n 0,nfg 13.3.1. Form error proleFor form error modeling for precision assessment, the initial workpiece surface prole is measured as y0m(x,z) before machining and y0g(x,z) = y0m(x,z).In this assessment, the surface form prole yn(x,z) will b
45、e used. For a full removal pass, n nfg, it is relatively easy to determine yn(x,z) using Eq. (1) for theoretical form error assessment. For a partial removal pass, nnfg, part of y0m(x,z) (Eq. (3) will be used in the assessment.For both cases, yn(x,z) is obtained through cng(x,z) as yn(x,z) = y0(x,z)
46、 cn(x,z). cng(x,z) is obtained based on Eq. (1).It is noted that, for a grinding pass of partial removal (Fig. 1(a)and (b), cng(x,z) will be positive or cng(x,z) 0 in the x positions(Fig. 1(a) and (b) that involve wheel and workpiece contact and cng(x,z) will be negative or cng(x,z) 0 for x 0,Lg, wh
47、ere Lg is the workpiece length (Fig. 1).As such, the surface form prole yng(x,z) for theoretical form error assessment can be expressed asEq. (3) is actually the model of partial removal condition, while the full removal condition model will be given in Eqs. (4) and (5).3.3.2. Grinding pass number n
48、fgTherefore, the grinding pass number nfg which is the rst grinding pass of full removal can be determined based on the size reduction cng(x,z) as3.3.3. Three stagesTherefore, we will have three stages:3.3.4. Form error assessmentFor simplicity, the peak-valley value of the surface form prole is used for form