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ANALYSIS OF POLYCRYSTALLINE DIAMOND MICRO-GRINDING TOOL TOPOGRAPHY USING IMAGE PROCESSING

机译:基于图像处理的多晶金刚石微磨工具层析成像分析

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Polycrystalline diamond (PCD) micro-grinding tools are shaped using different electro-discharge machining processes among which wire electro-discharge grinding (WEDG) process is widely accepted due to its capability of producing highly precise, ultra-thin and dimensionally accurate tools. Topographical analysis of the tool surface is inevitable as abrasive cutting edge density and grit protrusion heights affect the cutting process and are equally demanding parameters in modeling of the process. Analysis of micro-grinding tool topography and determining the tool surface statistics have always been a vital challenge due to random positioning and orientation of the abrasives on the tool surface. Current study deals with dressing of polycrystalline diamond micro-tool using wire-electro-discharge grinding and thereafter analysis of tool surface topography using novel image processing technique. In contrast to most of the previous experimental studies based on wheel replication and touch probe data to analyze the conventional grinding tool surface, we develop a sequential methodology using various available image processing algorithm tools and its application on the actual 3D surface data of the miniature sized PCD tool to capture the abrasive density and protrusion heights distribution. It is observed that image processing employing leveling, threshold, watershed algorithm and edge detection provided accurate outlook into the tool surface. Static cutting edge density is found to be 165-170 grits (per mm) and its variation with respect to depth into tool surface is also observed.
机译:多晶金刚石(PCD)微研磨工具是使用不同的放电加工工艺成型的,其中电火花线切割(WEDG)工艺因其能够生产高精度,超薄和尺寸精确的工具而被广泛接受。刀具表面的形貌分析是不可避免的,因为磨料的切削刃密度和砂砾突出高度会影响切削过程,并且在过程建模中同样要求苛刻的参数。由于磨料在工具表面上的随机定位和取向,分析微研磨工具的形貌并确定工具表面的统计数据一直是至关重要的挑战。目前的研究涉及使用线切割法修整多晶金刚石微型工具,然后使用新颖的图像处理技术对工具表面形貌进行分析。与以往大多数基于砂轮复制和测头数据分析传统磨具表面的实验研究相反,我们使用各种可用的图像处理算法工具开发了一种顺序方法,并将其应用于微型尺寸的实际3D表面数据PCD工具可捕获磨料密度和突出高度分布。可以看出,采用调平,阈值,分水岭算法和边缘检测的图像处理为工具表面提供了准确的外观。发现静态切削刃密度为165-170沙(每毫米),并且还观察到其相对于进入工具表面深度的变化。

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