Survey of effectiveness of inverse analysis computation

Survey of effectiveness of inverse analysis computation

Łukasz Sztangret, Danuta Szeliga, Jan Kusiak

AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków, Poland.

DOI:

https://doi.org/10.7494/cmms.2014.3.0487

Abstract:

The paper presents a survey of inverse analysis focusing on two aspects: computing time and accuracy of the solution. Identification of flow stress model in metal forming processes was considered as the inverse problem. This identification is usually performed by coupling the FE model with optimization techniques which leads to long computing times. Application of the metamodel instead of FE model in the inverse analysis was proposed as a solution of this problem. The second dilemma concerns the choice of the best optimization method. Several bio-inspired optimization algorithms were used in the inverse calculations. Comparison of obtained results is presented in the paper.

Cite as:

Sztangret, Ł., Szeliga, D., & Kusiak, J. (2014). Survey of effectiveness of inverse analysis computation. Computer Methods in Materials Science, 14(3), 160 – 166. https://doi.org/10.7494/cmms.2014.3.0487

Article (PDF):

Keywords:

Inverse analysis, Bio-inspired optimization methods, Metamodel

Publication dates:

Received: 23.07.2014, accepted: 14.11.2014, published:

Publication type:

Original scientific paper

References:

Arabas, J., 2001, Wykłady z algorytmów ewolucyjnych, Wydawnictwa Naukowo-Techniczne, Warszawa (in Polish).

Cytowski, J., 1996, Algorytmy genetyczne. Podstawy zastosowania, Akademicka Oficyna Wydawnicza PLJ, Warszawa (in Polish).

Deb, K., 2001, Multi-objective optimization using evolutionary algorithms, Chichester, London, Wiley.

Forestier, R., Massoni, E., Chastel, Y., 2002, Estimation of constitutive parameters using an inverse method coupled to a 3D finite element software, Journal of Materials Processing Technology, 125, 594-601.

Foryś, P., 2007, Nowy algorytm optymalizacji rojem cząstek jego zastosowanie w kształtowaniu elementów konstrukcji, PhD thesis, Politechnika Krakowska, Kraków (in Polish).

Gelin, J. C., Ghouati, O., 1994, An inverse method for determining viscoplastic properties of aluminum alloys, Journal of Materials Processing Technology, 45, 435-440.

Goldberg, D. E., 1995, Algorytmy genetyczne ich zastosowania, Wydawnictwa Naukowo-Techniczne, Warszawa (in Polish).

Hadamard, J., 1923, Lectures on the Cauchy Problem in Linear Partial Differential Equations, Yale University Press, New Haven.

Kennedy, J., Eberhart, R. C., 1995, Particle Swarm Optimiza- tion, Proc. IEEE International Conference on Neural Networks, Piscataway, 1941-1948.

Kusiak, J., Szeliga, D., Sztangret, Ł., 2012, Modelling tech- niques for optimizing metal forming processes, Micro- structure evolution in metal forming processes, eds. Lin, J., Balint, D., Pietrzyk, M., Woodhead Publishing Limited, Oxford, 35-66.

Kuś, W., Mucha, W., 2014, The idea of optimization strategy for industrial processes, Computer Methods in Materials Science, 14, 13-19.

Myers, R. H., Montgomery, D.C., 1995, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, Wiley, New York.

Schwefel, H.-P., 1995, Evolution and optimum seeking, Wiley, Chichester.

Szeliga, D., Gawąd, J.,Pietrzyk, M., 2006, Inverse analysis for identification of rheological and friction models in metal C N forming, Computer Methods in Applied Mechanics and C Engineering, 195, 6778-6798. L A R A M N D O H M R U P M O C – 166

Szeliga, D., Pietrzyk, M., 2007, Testing of the inverse software for identification of rheological models of materials subjected to plastic deformation, Archives of Civil and Mechanical Engineering, 7, 35-52.

Szeliga, D., Pietrzyk, M., 2010, Identification of rheological models and boundary conditions in metal forming, International Journal of Materials and Product Technology, 39, 388-405.

Szeliga, D., 2013, Identification problems in metal forming. A comprehensive study, AGH University of Science and Technology Press, Kraków.

Sztangret, Ł., Stanisławczyk A., Kusiak J., 2009, Bio-inspired optimization strategies in control of copper flash smelt- 400-408.

Sztangret, Ł., Szeliga, D., Kusiak, J., Pietrzyk, M., 2012, Application of the inverse analysis with metamodelling for the identification of the metal flow stress, Canadian Metallurgical Quarterly, 51, 440-446.

Sztangret, Ł., 2014, Redukcja nakładów obliczeniowych w optymalizacji procesów metalurgicznych, PhD thesis, Akademia Górniczo-Hutnicza, Kraków (in Polish).

Tadeusiewicz, R., 1993, Sieci neuronowe, Akademicka Oficyna Wydawnicza, Warszawa (in Polish).