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- 10 -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 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, 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 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 stage 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 estimation 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, 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, which 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 system 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 controlGrinding 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, 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 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), 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 contact 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 modeled 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 workpiece 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