Advertisement

Fixture layout optimization for flexible aerospace parts based on self-reconfigurable swarm intelligent fixture system

  • Li XiongEmail author
  • Rezia Molfino
  • Matteo Zoppi
ORIGINAL ARTICLE

Abstract

Based on the development demand of a novel intelligent fixture system, a self-reconfigurable intelligent swarm fixture system is presented. This paper deals with the fixturing layout optimization of a flexible aerospace workpiece. A new fixturing principle, “N-2-1-1,” is put forward. The optimization procedure for fixture layout combined with genetic algorithm and finite element analysis is developed and verified by case study simulation.

Keywords

Fixture layout optimization Finite element analysis Intelligent fixture Flexible workpiece 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cai W, Hu SJ, Yuan JX (1996) Deformable sheet metal fixturing: principles, algorithms, and simulations. ASME J Manuf Sci Eng 118:318–331CrossRefGoogle Scholar
  2. 2.
    Li B, Melkote SN (1999) Improved workpiece location accuracy through fixture layout optimization. Int J Mach Tool Manuf 39:871–883CrossRefGoogle Scholar
  3. 3.
    Krishnakumar K, Melkote SN (2000) Machining fixture layout optimization. Int J Mach Tool Manuf 40:579–598CrossRefGoogle Scholar
  4. 4.
    Kaya N (2006) Machining fixture locating and clamping position optimization using genetic algorithms. Comput Ind 57:112–120CrossRefGoogle Scholar
  5. 5.
    Vallapuzha S, De EC M et al (2002) An investigation into the use of spatial coordinates for the genetic algorithm based solution of the fixture layout optimization problem. Int J Mach Tool Manuf 42:265–275CrossRefGoogle Scholar
  6. 6.
    Afzeri, Ibrahim N (2008) Hybrid optimization of Pin type fixture configuration for free form workpiece. Int J Sci Eng Tech 2(3):33–40Google Scholar
  7. 7.
    Menassa R, DeVries W (1991) Optimization methods applied to selecting support positions in fixture design. J Eng Ind Trans ASME 113:412–418Google Scholar
  8. 8.
    Renner G, Ekart A (2003) Genetic algorithms in computer aided design. Comput Aided Des 35:709–726CrossRefGoogle Scholar
  9. 9.
    Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, ReadingzbMATHGoogle Scholar
  10. 10.
    T. Back (1993) Optimization mutation rates in genetic search. Proceedings of the Fifth International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, 1993. pp.2–9Google Scholar
  11. 11.
    Beasley JE, Chu PC (1996) A genetic algorithm for the set covering problem. Eur J Oper Res 94:392–404zbMATHCrossRefGoogle Scholar
  12. 12.
    Satyanarayana S, Melkote SN (2004) Finite element modeling of fixture-workpiece contacts: single contact modeling and experimental verification. Int J Mach Tool Manuf 44:903–913CrossRefGoogle Scholar
  13. 13.
    Liao YJ, Hu SJ, Stephenson DA (1998) Fixture layout optimization considering workpiece-fixture contact interaction: simulation results. Transaction of NAMRI/SME 26:341–346Google Scholar
  14. 14.
    Liao YG, Khetan R, Stevenson R (2000) An experimental investigation into the deflection of a fixture-workpiece system. Transaction of NAMRI/SME 28:413–418Google Scholar
  15. 15.
    Wiese K, Goodwin SD (1998) The effect of genetic operator probabilities and selection strategies on the performance of a genetic algorithm, advances in artificial intelligence. Lect Notes Comput Sci 1418:139–153CrossRefGoogle Scholar
  16. 16.
    Matteo Zoppi, Li Xiong, Rezia Molfino etc. (2010) The development of the locomotion subsystem of a self-reconfigurable mobile fixture, 2010 International Conference on Mechanism and Machine Science, CCMMS2010, ShanghaiGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.PMAR Lab., DIMECUniversity of GenoaGenoaItaly

Personalised recommendations