Optimization of grinding Process by Means of In-process Measurement of grinding wheel Surface

本研究的目的是通过砂轮表面的在线测量来优化磨削操作。该项目的概要如下。(1)现场车轮监测系统的建设。(2)表征砂轮表面的相应测量环法。(3)砂轮表面的在线评价。表征砂轮表面的参数分为两类：静态参数和动态参数。在后处理静态评价中，通过触针轮廓术和显微镜直接测量车轮表面。分别用触针式轮廓仪和数字显微镜测量了车轮表面的周向轮廓轨迹和二维图像。在这里，磨料颗粒和切削刃根据任意定义的标准进行识别。利用该系统可以测量磨粒密度、刃口密度、连续刃口间距、刃口比以及磨粒的微观形貌 关于我们 这是一个很好的准确度。该技术能够相对容易地测量砂轮表面形貌，从而可以通过判断磨削条件的合适性和砂轮重新布置的时机来控制磨削操作。另一方面，在过程动态测量中，通过分析磨削区中砂轮-工件相互作用产生的磨削声音来评估砂轮状态。通过合理修整形成砂轮表面的多个基准条件，并利用神经网络学习算法对基准砂轮表面发出的磨削声频谱进行识别。总体识别率至少为60%。该网络还将砂轮磨损识别为磨粒尖端变平的砂轮表面，这相当于较低修整进给量产生的砂轮磨损。因此，该系统可以即时识别车轮表面的差异。少

The purpose of this study is to optimize the grinding operation by means of in-process measurement of the wheel surface. The outlines of the project are as follows.(1) Construction of the in-situ wheel monitoring system.(2) Characterization of the wheel surface corresponding to the measure ring method.(3) In-process evaluation of the wheel surface.Parameters for characterizing grinding wheel surface are classified into two categories : static and dynamic parameters. In the post-process static evaluation, the wheel surface is directly measured by means of the stylus profilometry and the microscopy. The circumferential profile trace and 2D-image of a wheel surface is measured by the stylus profilometer and the digital microscope, respectively. Here abrasive grains and cutting edges are identified according to arbitrarily defined criteria. The grain density, cutting-edge density, successive cutting-edge spacing, cutting-edge ratio and micro-morphology of abrasive grains can be measured wi … More th a good degree of accuracy. This technique enables to measure the wheel topography relatively easily so that it has the possibility to control the grinding operation by judging the suitability of the grinding condition and the timing of re-arrangement of the wheel.In the in-process dynamic measurement, on the other hand, the wheel condition is evaluated by analyzing the grinding sound, which is generated by the wheel-work interaction in the grinding zone. Several reference conditions of the wheel surface are formed by the appropriate dressing and the frequency spectrums of grinding sound emitted from the reference wheel surface are discriminated by the neural network learning algorithm. The overall recognition rate is at least 60%. The network also recognize the wheel wear as the wheel surface with flattened grain tips, which is equivalent to that of the wheel generated with lower dressing feed. Accordingly this system can recognize instantaneously the difference of the wheel surface. Less