| Project/Area Number |
07555357
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| Research Category |
Grant-in-Aid for Scientific Research (A)
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| Allocation Type | Single-year Grants |
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| Section | 試験 |
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| Research Field |
機械工作・生産工学
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| Research Institution | The University of Tokyo |
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Principal Investigator |
NAGAO Takaaki The University of Tokyo, Faculty of Engineering, Professor, 大学院・工学系研究科, 教授 (80010685)
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| Co-Investigator(Kenkyū-buntansha) |
FUKUI Toyoharu Tokyo Seimitsu, Production Division, Measurement Group, Group Leader, 生産本部・汎用計測機器グループ, グループリーダ
NAKAO Masayuki The University of Tokyo, Faculty of Engineering, Associate professor, 大学院・工学系研究科, 助教授 (90242007)
HATAMURA Yotaro The University of Tokyo, Faculty of Engineering, Professor, 大学院・工学系研究科, 教授 (40010863)
MITSUISHI Mamoru The University of Tokyo, Faculty of Engineering, Associate Professor, 大学院・工学系研究科, 助教授 (90183110)
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| Project Period (FY) |
1995 – 1996
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| Project Status |
Completed (Fiscal Year 1996)
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| Budget Amount *help |
¥1,900,000 (Direct Cost: ¥1,900,000)
Fiscal Year 1996: ¥1,900,000 (Direct Cost: ¥1,900,000)
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| Keywords | Coordinate Measurement Machine / Thermal Deformation / Deformation Sensor / Variable Temperature Environment / Neural Network / Genetic Algorithm / On-One Site / Internal Monitoring / 広範囲高精度測定 / 原子間力顕微鏡 / 粗動機構 / 微動機構 / ピエゾアクチュエータ / ナノブロック / ニューラル・ネットワーク |
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| Research Abstract |
To measure a mechanical part in high accuracy, a coordinate measurement machine (CMM) is generally located in the constant temperature room. However, it is rather difficult to locate a manufacturing tool in the constant temperature room. Therefore, clamp and unclamp sequences are repeated for machining and measurement. It reduces the machining accuracy. To overcome the problem, a coordinate measurement machine with high accuracy even in the variable temperature condition was developed in this research.
Ten deformation sensors were attached on the structure of the CMM.As the deformation sensor is made of super-invar material with very low thermal expansion, only the thermal distortion of the machine itself is measured without influence of the thermal changes in the environment or in the machine structure.
In the experiment the standard gauge was made using super-invar material. To compensate for the thermal deformation, a measurement space compensation matrix was prepared. Each element of the compensation matrix was calculated by a neural network integrated with genetic algorithm. The input to the neural network is the output from deformation sensors and temperature information of air and optical scales.
Edge length and angle between two edges were maintained less than 15% and 30%, respectively.
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