1994 Fiscal Year Final Research Report Summary
AI control system for press forming of of Aluminum alloy sheet for automotive body
| Project/Area Number |
04452274
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| Research Category |
Grant-in-Aid for General Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
金属加工(含鋳造)
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| Research Institution | Tokyo Metropolitan University |
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Principal Investigator |
NISHINUMA Hisashi Tokyo Metropolitan University, department of Mechanical Engineering, Professor, 工学部, 教授 (70087170)
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| Co-Investigator(Kenkyū-buntansha) |
YANG Ming Tokyo Metropolitan University, department of Mechanical Engineering, Research As, 工学部, 助手 (90240142)
MANABE Kenichi Tokyo Metropolitan University, department of Mechanical Engineering, Associate P, 工学部, 助教授 (10145667)
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| Project Period (FY) |
1992 – 1994
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| Keywords | Aluminum alloy / AI control system / Press forming / Database / Formability / Material for automotive body / Intelligent tool system / 成形温度依存性 |
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| Research Abstract |
The press formability of the aluminum alloy sheet of automotive body upon the strain rate, temperature, tool conditions and lubricants is investigated experimentally and the results are stored into an database.
Furthermore, optimum control systems for the press forming are developed based on the database and AI control model.
1) Press formability of aluminum alloy
The basic formability of the material is tested by tensile tests with several kinds of strain rates and temperatures. The formability increases at higher temperature and lower strain or with lower temperature and higher strain rate, but decreases at other conditions. Deep drawing tests are performed by corresponding strain rate and temperatures and several kinds of lubricants and tool conditions. The formability of the deep drawing improved by increasing the forming speed on which the strain rate and lubricant depend and is also improved according to the increase of the temperature.
2) AI control system
AI control systems are deve
… More
loped for two typical press forming, bending and deep drawing, respectively. An AI prediction control system based on an on-line database is developed for the V-bending process. The on-line measured punch force-stroke curve is compared with the retrieval from the database directly and to predict the control value by a fuzzy model. An optimum control system is also developed to control the variable BHF of deep drawing process according The two evaluation functions. Furthermore, concept of intelligent tool system is proposed and the process control of L-bending and deep drawing with the intelligent tool systems are developed. the results show that not only accuracy of the process is improved signifficantly but also the flexibility of coping with the scattering in material properties and process conditions is proved.
3) Practicability
The developed database and AI control system are on a basis of the industrial production and may be introduced to real production by fulling the database and modifying the control system upon the scaled forming. Less
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