Immune and evolutionary shape optimization in forging
Wacław Kuś1
, Tadeusz Burczyński1,2
1Department for Strength of Materials and Computational Mechanics Silesian University of Technology, Konarskiego 18a, 44-100 Gliwice, Poland.
2Institute of Computer Modelling, Artificial Intelligence Section Cracow University of Technology, Warszawska 24, 31-155 Kraków, Poland.
DOI:
https://doi.org/10.7494/cmms.2008.2.0194
Abstract:
The paper deals with applications of methods of artificial intelligence: artificial immune systems and evolutionary algorithms in optimization of a forging process. The shape optimization of the anvils in a two-stage forging process is considered as a numerical example. The paper contains description of the evolutionary algorithm, the artificial immune system and parallel versions of bioinspired algorithms in grid environment.
Cite as:
Kuś, W., Burczyński, T., (2008). Immune and evolutionary shape optimization in forging. Computer Methods in Materials Science, 8(2), 79 – 85. https://doi.org/10.7494/cmms.2008.2.0194
Article (PDF):
Keywords:
Evolutionary computing, Immune computing, Grid environment, Shape optimization, Forging process
References:
Akshay, L., Rajkumar, R., Srikumar, V., 2005, The Alchemi.NET-based Adaptive Computing Platform, High Performance Computing and Communications HPCC 2005, Lecture Notes on Computational Science 3726, Springer.
Alchemi web page, 2002, http://sourceforge.net/projects/alchemi.
António, C.A.C., Douardo, N.M., 2002, Metal-forming optimization by inverse evolutionary search, J. Mat. Proc. Techn., 121, 403-413.
Badrinarayanan, S., 1997, Preform and die design problems in metalforming, PhD. thesis, Cornell University.
Burczyński, T., Kuś, W., Beluch, W., John, A., Poteralski, A., Szczepanik, M., 2004, Zastosowanie algorytmów ewolucyjnych z MSC/Nastran i MSC/Marc w zagadnieniach optymalnego projektowania, Biuletyn WAT, nr 6 (622), (in Polish).
Burczyński, T., Kuś, W., Poteralski, A., Szczepanik, M., 2007, Global optimization using artificial immune systems and comparison with evolutionary algorithms, Proc. 17-th Int. Conf. on Computer Methods in Mechanics CMM2007, Lódź-Spała, CD ROM.
Chung, J.S., Hwang, S.M., 1998, Application of a genetic algorithm to process optimal design in non-isothermal metal forming, J. Mat. Proc. Techn., 80-81.
de Castro, L. N., Timmis, J., 2003, Artificial Immune Systems as a Novel Soft Computing Paradigm, Soft Computing, 7(8), 526-544.
de Castro, L.N., Von Zuben, F.J., 2002, Learning and Optimization Using the Clonal Selection Principle, IEEE Transactions on Evolutionary Computation, Special Issue on Artificial Immune Systems, 6:3, 239-251.
Kuś, W., Burczyński, T., 2006, Grid-based evolutionary optimization of structures, Parallel Processing and Applied Mathematics PPAM 2005, Lecture Notes on Computational Science 3911, Springer.
Kuś, W., 2006, Evolutionary optimization of forging anvils using grid based on Alchemi framework, Proc. 2nd IEEE International Conference on e-Science and Grid Computing eScience 2006, Amsterdam.
Kuś, W., Burczyński, T., 2007a, Biological inspired methods for shape optimization in grid environments, Proc. 7th World Congress on Structural and Multidisciplinary Optimization, WCSMO-7, Seul, CD-ROM.
Kuś, W., Burczyński, T., 2007b, Applications of parallel artificial immune systems in shape optimization, Proc. 17-th Int. Conf. on Computer Methods in Mechanics CMM2007, Lódź-Spała, CD ROM.
Michalewicz, Z., 1996, Genetic algorithms + data structures = evolutionary algorithms. Springer-Verlag, Berlin.
Users Guide, 2002, MSC.Marc.
Wierzchoń, S. T., 2001, Artificial Immune Systems, theory and applications, EXIT, Warsaw.
Zabaras, N., Bao, Y., Srikanth, A., Frazier W. G., 2000, A continuum Lagrangian sensitivity analysis for metal forming processes with applications to die design problems, Int. J. Numer. Meth. Engng., 48.
Zhao, X., Zhao, G., Wang, G., Wang, T., 2002, Preform die shape design for uniformity of de-formation in forging based on preform sensitivity analysis, J. Mat. Proc. Techn., 128, 25-32.