Transactions of Materials Processing (소성∙가공)

The Korean Society for Technology of Plasticity and materials processing (한국소성∙가공학회)
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A Study on Optimal Process Design of Hydroforming Process with n Genetic Algorithm and Neural Network

Genetic Algorithm과 Neural Network을 이용한 Tube Hydroforming의 성형공정 최적화에 대한 연구

  • Published : 2000.11.01
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Abstract

Tube hydroforming is recently drawing attention of automotive industries due to its several advantages over conventional methods. It can produce wide range of products such as subframes, engine cradles, and exhaust manifolds with cheaper production cost by reducing overall number of processes. h successful tube hydroforming depends on the reasonable combination of the internal pressure and axial load at the tube ends. This paper deals with the optimal process design of hydroforming process using the genetic algorithm and neural network. An optimization technique is used in order to minimize the tube thickness variation by determining the optimal loading path in the tube expansion forming and the tube T-shape forming process.

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References

  1. Journal of Materials Processing Technology v.46 Hydromechanical drawing of automotive parts S. Nakamura;H. Sugiura;H. Onoe;K. Ikemoto
  2. Innovations In Hydroforming Technology Tube Hydroforming : Advancement Using Sequenced Forming Pressures M. Mason
  3. Journal of Materials Processing Technology v.71 Tube hydroforming-research and practial application F. Dohmann;Ch. Hartl,
  4. SAE, International Congress and Exposition Tube hydroforming-state-of-the-art and future trends M. Ahmetoglu;T. Altan
  5. Numisheet'99 Simulation of tube hydroforming for an automotive part J. B. Yang;B. H. Jeon;S. I. Oh
  6. TPA's 2nd Annual Automotive Tube Conference Introduction to Tube Bending for Hydroforming Applications Laurence Oldcre
  7. Annals of the CIRP v.41 Pressure Control in Hydraulic Tube Forming D. Schmoeckel;C. Hessler;B. Engel
  8. Advanced Technology of Plasticity Finite Element Analysis of the Liquid Buldge Forming Processes H.J. Kim;B.H. Jeon;H.Y. Kim;J.J. Kim
  9. Report No.ERC/NSM-R-98-25 Evaluation of tube formability and material characteristics in tube hydroforming T. Sokolowski;T. Altan
  10. Advanced Technology of Plasticity Deformation of Metal Tubesunder Hydrostatic Bulge Forming with Closed Die S. Fuchizawa
  11. TPA's 2nd Annual Automative Tube Conference Hydroforming Product and Process : Requirements and Implementations C. Brruggemann;S. Shah
  12. Numisheet'99 Control techniques for the hydroforming of tubes and flanges O. Ghouati;H. Lenoir;P. Paquier;J. C. Gelin,
  13. Introduction to Optimum Design J. S. Arora
  14. Genetic Algorithms and Evolution Strategy in Engineering and Computer Science D. Quagliarella
  15. SPIE Proc. v.1196 Micro-genetic algorithms for stationary and non-stationary function optimization K. Krishnakumar
  16. Neural networks S. Haykin