Development of a soft computing-based framework for engineering design optimisation with quantitative and qualitative search spaces

Applied Soft Computing Journal

Volumn 7, Issue 1, 2007, Pages 166-188

  Oduguwa, Victor ^a   Roy, Rajkumar ^a   Farrugia, Didier ^b  

^a CRANFIELD UNIVERSITY   (United Kingdom)

^b SWINDEN TECHNOLOGY CENTRE   (Netherlands)

Author keywords

Multi objective optimisation; Quantitative and qualitative search space; Rolling system design; Soft computing application

Indexed keywords

COMPUTER AIDED DESIGN; INFORMATION ANALYSIS; MATHEMATICAL MODELS; OPTIMIZATION; PROBLEM SOLVING;

MULTI-OBJECTIVE OPTIMIZATION; QUANTITATIVE AND QUALITATIVE SEARCH SPACE; ROLLING SYSTEM DESIGN; SOFT COMPUTING APPLICATION;

COMPUTATION THEORY;

EID: 33751088019     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2005.06.003     Document Type: Article

References (48)

1. Allen T.T., Bernshteyn M.A., and Kabiri K. A comparison of alternative methods for constructing meta-models for computer experiments. J. Qual. Technol. (2003)
2. Antonsson E.K., and Wood K.L. Computations with imprecise parameters in engineering design: background and theory. J. Mech. Trans. Autom. Des. 111 (1989) 616-625
3. Bellman R.E., and Zadeh L.A. Decision making in a fuzzy environment. Manage. Sci. 17 4 (1970) B141-B164
4. Bodenhofer U., and Herrera F. Ten Lectures on Genetic Fuzzy Systems (1997), Software Competence Center, Hagenberg
5. Chakraborti N., and Kumar A. The optimal scheduling of a reversing strip mill: studies using multi-population genetic algorithms and differential evolution. Mater. Manuf. Process. 18 (2003) 307
6. Chung J.S., and Hwang S.M. Process optimal design in forging by genetic algorithm. J. Manuf. Sci. Eng. 124 (2002) 397-408
7. Coiera E. The qualitative representation of physical system. Knowl. Eng. Rev. 7 1 (1992) 34
8. Dahal K.P., Aldridge C.J., and McDonald J.R. Generator maintenance scheduling using genetic algorithm with a fuzzy evaluation function. Fuzzy Set Syst. 102 (1999) 21-29
9. K. Deb, S. Agrawal, A. Pratap, T.A. Meyarivan, Fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II, Parallel Problem Solving from Nature VI (PPSN-VI), 2000, pp. 849-858.
10. Farrugia D. Advanced Analysis System for Roll Pass Design (2000), 11th Abaqus User's Group, Warrington, UK
11. Fonseca C.M., and Fleming P.J. Proceedings of the Fifth International Conference on Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization. Morgan Kaufmann, San Mateo, CA (1993) 416-423
12. Goldberg D.E. Genetic Algorithm in Search, Optimization and Machine Learning (1989), Addison Wesley, Massachusetts
13. Hsu Y., Liu T., Hsu M., and Thibult F. A fuzzy optimization algorithm for blow moulding process. Proceedings of the Sixth International Conference on CSCW Design. Ontario, Canada (2001)
14. Hwang S.M., and Chung J.S. Application of genetic algorithm to optimal design of the die shape in extrusion. J. Mater. Process. Technol. 72 (1997) 69-77
15. Jung J.Y., and Im Y.T. Fuzzy algorithm for calculating roll speed variation based on roll separating force in hot rolling. Int. J. Mech. Sci. 42 2 (2000) 249-272
16. Kang B.-S., Lee J.-H., and Lee J.-H. Process design in multi-stage cold forging by the finite element method. J. Mater. Process. Technol. 58 (1996) 174-183
17. Kim N.S., Kobayashi S., and Altan T. Three-dimensional analysis and computer simulation of shape rolling by the finite and slab element method. Int. J. Mach. Tool Manuf. 31 (1991) 553-563
18. Knowles J.D., and Corne D.W. The Pareto archived evolution strategy: a new baseline algorithm for multiobjective optimisation. Proceedings of the 1999 Congress on Evolutionary Computation. New Jersey (1999) 98-105
19. Koskimaki E., and Goos J. Fuzzy fitness function for electric machine design by genetic algorithm. Proceedings of the Second Nordic Workshop on Genetic Algorithms and their Applications (2NWGA). Vaasa, Finland (1996) 237-244
20. Lenard J.G., Said A., Ragab A.R., and Abo Elkhier M. The temperature, roll force and roll torque during hot bar rolling. J. Mater. Process. Technol. (1997) 147-153
21. Medaglia A.L., Fang S.-C., and Nuttle H.L.W. Fuzzy controlled simulation optimization. Fuzzy Set Syst. 127 (2002) 65-84
22. Michalski R.S. A theory and methodology of inductive learning. In: Michalski R.S., Mitchel T.W., and Mitchell T.M. (Eds). Machine Learning: An Artificial Intelligence Approach (1983), Morgan Kaufmann 83-134
23. Montgomery D.C., and Peck E.A. Introduction to Linear Regression Analysis (1992), Wiley, New york
24. Montgomery D.C. Design and Analysis of Experiments. 4th ed. (1997), John Wiley & Sons p. 700
25. Mori K. General purpose FEM simulator for 3D rolling. Adv. Technol. Plasticity 4 (1990) 1773-1778
26. Myllykoski P., Larkiola J., Korhonen A.S., and Cser L. The role of neural networks in the optimisation of rolling processes. J. Mater. Process. Technol. 80-81 (1998) 16-23
27. Nijkamp P., Rietveld P., and Voogd H. Multicriteria Evaluation in Physical Planning (1990), North-Holland, Amsterdam
28. V. Oduguwa, Rolling System Design Optimisation using Soft Computing Techniques, Eng.D. Thesis, Cranfield University, Cranfield, UK, 2003.
29. Oduguwa V., Roy R., and Farrugia D. Fuzzy multi-objective optimisation approach for rod shape design in long product rolling. Proceedings of the IFSA'03. Istanbul, Turkey (2003)
30. Oduguwa V., Roy R., Watts G., Wadsworth J., and Farrugia D. Comparison of low-cost response surface models for rod rolling design problems. Proceedings of the 18th National Conference on Manufacturing Research. Leeds Metropolitan University, Professional Engineering Publishing, UK (2002) 209-213
31. Oduguwa V., Tiwari A., and Roy R. Genetic algorithm in process optimisation. Proceedings of the Eighth Online World Conference on Soft Computing in Industrial Applications (WSC-8) (CDROM). Finland (2003)
32. Pataro C.D.M., and Helman H. Direct determination of sequences of passes for the strip rolling process by means of fuzzy logic rules. Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials (IPMM'99) (1999) 549-554
33. Pedrycz W. Fuzzy control and Fuzzy Systems (1989), Wiley, New York
34. R. Roy, Adaptive Search and the Preliminary Design of Gas Turbine Blade Cooling System, Ph.D. Thesis, University of Plymouth, Plymouth, 1997.
35. Sacks J., Welch W.J., Mitchell T.J., and Wynn H.P. Design and analysis of computer experiments. Stat. Sci. 4 4 (1989) 409-435
36. Schaffer J.D. Multiple objective optimization with vector evaluated genetic algorithms. Proceedings of the First International Conference on Genetic Algorithms. Pittspurgh (1985) 93-100
37. Shin H.W., Kim D.W., and Kim N.S. A study on the rolling of I-section beams. Int. J. Mach. Tool Manuf. 34 (1994) 147-160
38. Shivpuri R., and Kini S. Application of fuzzy reasoning techniques for roll pass design optimisation. Proceedings of the 39th Mechanical Working and Steel Processing Conference and Symposium on New Metal Forming Processes (1998) 755-763
39. Simpson T., Korte J., Mauery T., and Mistree F. Comparison of response surface and kriging models for multidisciplinary design optimization. Proceedings of the Seventh AIAA/UASF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization. Institute of Aeronautics and Astronautics (1998) 381-391
40. Smith M. Neural Networks for Statistical Modeling (1993), Von Nostrand, Reinhold, New York
41. Sun C.G., Yun C.S., Chung J.S., and Hwang S.M. Investigation of thermo-mechanical behavior of work roll and a roll life in hot strip rolling. Metall. Mater. Trans. A 29A (1998) 2407-2424
42. Tanaka H., Okuda T., and Asai K. On fuzzy mathematical programming. J. Cybern. 3 (1974) 37-46
43. A. Tiwari, Evolutionary computing techniques for handling variable interaction in engineering design optimisation, Ph.D. Thesis, Cranfield University, Cranfield, UK, 2001.
44. D.V. Veldhuizen, Multiobjective Evolutionary Algorithms: Classifications, Analysis, and New Innovations, Ph.D. Thesis, Air Force Institute of Technology, OH, Dayton, 1999.
45. Yoshida S., and Kihara S. Some experiences of shape-forming: experiments and numerical simulations. Eng. Comput. 13 2-4 (1996) 98-110
46. Zadeh L. Information and control. Fuzzy Set 8 3 (1965) 338-353
47. Zimmermann H.-J. Proceedings of the XXI International TIMS and 46th Conference on Optimization in Fuzzy Environment. San Juan, Puerto Rico (1974)
48. Zitzler E., and Thiele L. An Evolutionary Algorithm for Multiobjective Optimization: The Strength Pareto Approach (1998), Computer Engineering and Networks Laboratory, Zurich, Switzerland