1995 Fiscal Year Final Research Report Summary
Development of an intelligent deep-hole boring tool
| Project/Area Number |
06650304
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
Intelligent mechanics/Mechanical systems
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| Research Institution | KYUSHU UNIVERSITY |
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Principal Investigator |
KATSUKI Akio Kyushu University, Faculty of Engineering Research Associate, 工学部, 助手 (20038095)
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| Co-Investigator(Kenkyū-buntansha) |
MOHRI Akira Kyushu University, Faculty of Engineering Professor, 工学部, 教授 (50037909)
SAJIMA Takao Kyushu University, Faculty of Engineering Research Associate, 工学部, 助手 (20215750)
ONIKURA Hiromichi Kyushu University, Faculty of Engineering Professor, 工学部, 教授 (90108655)
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| Project Period (FY) |
1994 – 1995
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| Keywords | Deep hole boring tool / Intelligent / Guiding / Laser / Surface roughness / Tool wear / Sound / Artificial neural network |
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| Research Abstract |
The research was conducted to bore high-quality ultra-deep-hole (L/D*1000) by monitoring the cutting conditions, i.e., by giving functions of such five senses as sight, hearing, touch, taste and smell to a deep-hole boring tool. The following three items were made clear. (1) The old-type laser-guided deep-hole boring tool was improved to be of high-performance. The original tool had a He-Ne laser in the optical system for detecting its attitude and used cams as actuators. The new one is equipped with an argon laser and piezoelectric translaters. Basic experiments using duralumin workpieces (JIS A2017-T4) with a prebored 108 mm diameter hole showed that the new tool has high performance in manipulation and guidance. (2) The surface roughness could be simply characterized on the off-line information processing system by observation through an industrial endoscope, using a CCD camera and image processing. The chips exhausting-and tip-conditions could be observed on-line using image processing. (3) To detect the abnormal cutting, an Artificial Neural Network was constructed using cutting sound as input. Experiments were conducted on a lathe to examine whether the ANN can classify cutting conditions of chattering vibration, formation of multi-cornered hole and tool wear, which are expected to occur in deep hole boring. As a result, the cutting conditions couled be clasified off-line. Evaluation of the total deep-hole boring system using ANN continues hereafter.
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