2002 Fiscal Year Final Research Report Summary
Neural Network Based Quantitative Prediction of Corrosion Phenomena in Cars and Their Anti-Corrosion Design
| Project/Area Number |
12450043
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Materials/Mechanics of materials
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| Research Institution | The University of Tokyo |
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Principal Investigator |
YOSHIMURA Shinobu The University of Tokyo, Graduate School of Frontier Sciences, Professor, 大学院・新領域創成科学研究科, 教授 (90201053)
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| Co-Investigator(Kenkyū-buntansha) |
NAGASAKI Shinya The University of Tokyo, Graduate School of Frontier Sciences, Associate, 大学院・新領域創成科学研究科, 助教授 (20240723)
FURUTA Kazuo The University of Tokyo, Graduate School of Frontier Sciences, Professor, 大学院・新領域創成科学研究科, 教授 (50199436)
YAGAWA Genki The University of Tokyo, Graduate School of Engineering, Professor, 大学院・工学系研究科, 教授 (40011100)
KANTO Yasuhiro Toyohashi University of Technology, School of Engineering, Associate Professor, 機械システム工学科, 助教授 (60177764)
HORIE Tomoyoshi Kyushu institute of Technology, Faculty of Computer Science & Systems Engineering, Professor, 情報工学部, 教授 (40229224)
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| Project Period (FY) |
2000 – 2002
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| Keywords | Corrosion / Cars / Life Time Prediction / Neural Network / Multi-variate Analysis / Car Parameters / Corrosion Parameters / Anti-Corrosion Design |
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| Research Abstract |
In this research, we have developed a method to quantitatively predict long-term corrosion occurring m cars, and additionally the method to perform anti-corrosion design. As the key technology, a multilayered neural network is employed to analyze a number of case studies of practical corrosion in cars. The process of the developed method is as follows.
(1)At first, a number of case studies of corrosion of cars are given to a multilayered neural network, which is trained. Performance of the trained network in accuracy of learning and training and its network topology are investigated in detail.
(2)Sensitivity studies are performed using the trained network. Here, various kinds of corrosion parameters are considered for the sensitivity study. Influential parameters for corrosion phenomena are then selected.
(3)The case studies are again learned using the compacted neural network with the selected parameters. Its performances are again investigated in detail.
(4)Using the trained compacted neural network, solutions for anticorrosion design of cars are effectively searched and visualized as a multidimensional design window.
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