机械专业外文翻译平面磨削中形位误差的改进型离散系统模型.docx

1、机械专业外文翻译平面磨削中形位误差的改进型离散系统模型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-process sensing; Precision control; Model; Form errorAbstract:Grinding remains as one of few choices being ab

2、le 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, few studies on form error prediction have been reported. Machining usually begins with partial removal of

3、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 study, an improved discrete system model and an in-process sensing technique have been proposed to address

4、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 stage has been investigated. It is found that the grinding constant is able to reect changes in machining cond

5、itions 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 estimation of the grinding constant, two approaches are proposed. The iterative approach was found more suitab

6、le 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, surface form error Epvn and normal grinding force Fnn. Through detailed examination and comparative studies

7、, 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, which was indirectly measured through a voltage measurement approach, the overall relative errors between the t

8、heoretical 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 system model. The experimental results can be used as a set of references for further studies to offer perfor

9、mance assessment, precision prediction, process planning, and process condition monitoring for this important precision machining process.1. Introduction1.1. Model for precision controlGrinding is an abrasive precision machining process which remains as one of few choices being able to machine very

10、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, and optical parts (Karpuschewski and Inasaki, 2006). Surface grinding is one for precision machining of surf

11、aces. 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 multiple aspects (Baasz and Krlikowski, 2007), such as model of grain (Horng, 2008; Mamalis et al., 2001),

12、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), model of chip formation (Gopal and Rao,2004; Hecker et al., 2007), model of force (Hekman and Liang, 1999;J

13、enkins 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 contact mechanics. The elasticplastic effects in the wear mechanism were considered to be related to the density

14、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 modeled using fractal functions (Bigerelle et al., 2005). A roughness prediction model for wheel topography, wear,

15、 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 workpiece and chip. The temperature of the workpiece surface in the grinding zone could be predicted. A dynamic

16、 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 grinding process was proposed by Zhang et al. (2005). A chip thickness model by Gopal and Rao (2004) was de

17、veloped 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, predictive models for grinding force and power were proposed by Hecker et al. (2007).To control the depth of cut, a

18、 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 model for surface grinding (Tang et al.,2008) involved process parameter effects on friction coefcient. For

19、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 roughness, power, heat, chip thickness, and dynamic characteristics in a grinding process. Form error, as one of

20、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 grinding can be found. 1.3. Partial removal problemFor a typical surface grinding process, due to initial surfa

21、ce 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 in our previous model (Huang and Gao, 2010), we will experience a signicant amount of error in prediction of

22、 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, 1998), could be reduced.1.4. In-process sensing problemIn our previous model (Huang and Gao, 2010), in-proc

23、ess 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 parameters and we could not acquire accurate surface form information, which could prevent us from modeling

24、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, 2010), the machining process was not fully modeled. The maximum modeling error was up to 19.9% (Huang and G

25、ao, 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, 1993; Huang and Gao, 2010), a new in-process surface form prole measurementprototype system (Gao et al., 2009,

26、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, which is proposed for the surface grinding process. The improved discrete system model will be examined.The

27、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. In addition, the improved model will allow advanced control to achieve high efciency in ultra precision and

28、 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) (Fig. 1) will be used to enhance modeling accuracy. Without the in-process form error measurement (Figs. 1

29、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 to be mounted again after the ofine measurement (Huang and Gao,2010). This is particularly true for nonferrou

30、s 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), a new optical in-process surface form measurement prototype system has been developed and has been used o

31、n 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 sensor is Cyber Optics DRS300. It has a resolution of 50 nm and is used to measure workpiece surface form prole y

32、n(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 measurement. The air piece is to allow an air beam to be ejected and then impinged on the workpiece surface to re

33、move 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 al., 2010).2.2. Opaque barrier and vibrationDue to two key problems, which are opaque barrier and vibration (Gao