1992 Fiscal Year Final Research Report Summary
Development of an Intelligent System for Sheet Metal Bending
| Project/Area Number |
03555026
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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 |
機械工作
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| Research Institution | KYOTO UNIVERSITY |
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Principal Investigator |
SHIMA Susumu Kyoto Univ., Fac.Eng., Prof., 工学部, 教授 (70026160)
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| Co-Investigator(Kenkyū-buntansha) |
NAKAMURA Yoshimitsu Matsushita Electric Co., Ltd. Chief Engineer, 生産技術研究所, 技師長
YANG Ming Tokyo Met.Univ.Assistant, 工学部, 助手 (90240142)
ENDOH Jyunichi Kanagawa Inst.Tech., Prof., 教授 (70016418)
WATANABE Tohru Ritsumeikan Univ., Prof., 理工学部, 教授 (70026136)
YAMAGUCHI Katsuhiko Kyoto Inst.Tech., Prof., 工芸学部, 教授 (90027805)
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| Project Period (FY) |
1991 – 1992
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| Keywords | Bending / Metal Forming Control / Neural Network / Flexible Forming |
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| Research Abstract |
This project is to develop an intelligent metal forming system, in particular a common sheet metal bending, that performs flexibility in production and high accuracy incorporating feed forward control. By using a newly developed bending tool set which is suitable for displacement control, deformation of sheet metal is measured on-line ; the material properties was estimated by the measured data and combining with high accuracy of the tool displacement was performed. Further, a neural network was incorporated in the control system for further improvement of accuracy.
(1) Instead of conventional V-angled bending tool, the newly developed tools of circular or elliptical shape was used to bend sheet metals on a prototype bending machine. It was thus confirmed that flexibility was improved and the accuracy was also improved by one fold. This is due to the learning control system.
(2) In the above tool est, the contact point between the workpiece and the tool shifts according to the tool displacement. An intelligent sensor that measures the forces, both normal and tangential directions, was developed. The contact point and thus bend angle can be measured.
(3) A neural network, which describes complicated phenomena was incorporated in the system to develop a control model with high accuracy. The material properties estimated by the deformation model was input into the neural network and binding condition was thus determined. It was thus concluded that combination of plasticity model and neuro-model has its potential of an efficient control model with high accuracy.
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