1994 Fiscal Year Final Research Report Summary
System of Detecting Abnormal Cutting using Neural Network
| Project/Area Number |
05555072
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| Research Category |
Grant-in-Aid for Developmental Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Dynamics/Control
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| Research Institution | Toyota College of Technology |
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Principal Investigator |
KONDO Eiji Toyota College of Technology, Department of Mechanical Engineering, Associate Professor, 機械工学科, 助教授 (10183352)
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| Co-Investigator(Kenkyū-buntansha) |
KAWAI Tadao Nagoya University, Faculty of Engineering, Department of Mechanical Engineering,, 工学部・機械工学科, 講師 (20177637)
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| Project Period (FY) |
1993 – 1994
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| Keywords | Machine Tool / Lathe / Workpiece / Neural Network / Vibration Diagnosis / Spectral Analysis / Chatter Vibrations / Tool Wear |
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| Research Abstract |
The object of this study is to develop a system of detecting abnormal cutting for turning using spectral analysis and neural network. This system learns and recognizes patterns of the spectrum of cutting tool vibration using neural network. In 1993, the following things were carried out : (1) equipment of the detection system was designed and set up, (2) a method of detecting regenerative chatter using spectral analysis was presented and verified, (3) a method of detecting chatter vibrations using spectral analysis and neural network was presented and verified. The cutting tests were carried out at cutting speed of 10 to 140 m/min and 1.0 to 4.0 mm in width of cutting. And vibration of workpiece, dynamic cutting forces and vibration of cutting tool were measured at a cutting speed as changing the width of cutting discontinuously where self-excited chatter vibrations occurred or not. By the method using only spectral analysis, the rate of successful detection from vibration signal of wo
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rkpiece with little noise was 100%, but that from signal of dynamic thrust force with much noise was 71%. On the other hand, by the method using both spectral analysis and neural network, the rate of detection from vibration signal of workpiece was 88%, and that from dynamic thrust force was 83%. As a result, we concluded that the rate of successful detection by the method using both spectral analysis and neural network is about more than 80%, and this rate is hardly affected by the intensity of noise component. In 1994, a method of identifying width of tool flank wear using both spectral analysis and neural network was proposed and verified. On experiments using four kinds of artificial worn tools, workpieces were orthogonally cut by each tool having different flank wear in width. As a result, the rate of successful identification from vibration of cutting tool was about 80% under cutting conditions learned by the neural network, but that was about 30% under unlearned cutting conditions. Therefore we concluded that present method of identifying tool flank wear has to be improved. Less
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Research Products
(10 results)
