| Project/Area Number |
10650117
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
機械工作・生産工学
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| Research Institution | Kanazawa University |
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Principal Investigator |
HOSOKAWA Akira Kanazawa University Faculty of Engineering Associate Prof., 工学部, 助教授 (40199493)
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| Co-Investigator(Kenkyū-buntansha) |
YAMADA Keiji Kanazawa University Faculty of Engineering Research associate, 工学部, 助手 (50242532)
UEDA Takashi Kanazawa University Faculty of Engineering Professor, 工学部, 教授 (60115996)
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| Project Period (FY) |
1998 – 2000
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| Project Status |
Completed (Fiscal Year 2000)
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| Budget Amount *help |
¥1,600,000 (Direct Cost: ¥1,600,000)
Fiscal Year 2000: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 1999: ¥800,000 (Direct Cost: ¥800,000)
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| Keywords | Grinding / Wheel surface / Grinding wheel topography / In-process measurement / Image processing / Wheel life / Grinding sound / Neural network / 目直し / 砥粒切れ刃 / 形直し |
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| Research Abstract |
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
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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
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