Hybrid computer system for the identification of metallic material models on the basis of laboratory experiments

Łukasz Rauch; Grzegorz Górecki; Maciej Pietrzyk

doi:10.7494/csci.2016.17.2.123

Authors

Łukasz Rauch AGH University of Science and Technology
Grzegorz Górecki AGH University of Science and Technology
Maciej Pietrzyk AGH University of Science and Technology

DOI:

https://doi.org/10.7494/csci.2016.17.2.123

Keywords:

Inverse analysis, identification of model parameters, computer system, rings compression

Abstract

The identification of the proper parameters of material models plays a crucial role in the design of production technologies, especially in the case of modern materials with diversified properties under different boundary conditions. The procedure of identification is usually based on an optimization algorithm that uses sophisticated numerical simulations as a part of the goal function and compares the obtained results with experimental tests. Despite its reliability, such an approach is numerically inefficient. This paper presents the concept of how to replace the most numerically-demanding part of the identification procedure with metamodels, allowing us to maintain uniform result quality. The computer system, which allows us to manage input data, metamodels, and calculations, is proposed and described in detail in this paper. Finally, the proposed approach is validated on the basis of tests performed in the laboratory.

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References

Cheng K., Harrison D., Pan P.: Implementation of agile manufacturingan {AI} and Internet based approach. Journal of Materials Processing Technology, vol. 76(1-3), pp. 96–101, 1998, ISSN 0924–0136, http://www.sciencedirect.com/science/article/pii/S0924013697003294.

Cockcroft M., Latham D.: Ductility and the workability of metals. Journal of the Institute of Metals, vol. 96, pp. 33–39, 1968.

Danuta S., Maciej P.: Chapter 12 – Identification of Rheological and Tribological Parameters. In: J. Lenard, ed., Metal Forming Science and Practice, pp. 227–258, Elsevier Science Ltd, Oxford, 2002, ISBN 978-0-08-044024-8, http://www.sciencedirect.com/science/article/pii/B9780080440248500126.

Garbarz B., Burian W., Woniak D.: Semi-industrial simulation of in-line thermomechanical processing and heat treatment of nano-duplex bainite-austenite steel. Steel Research International, pp. 1251–1254, 2012, spec. issue conf. Metal Forming.

Giachetti R.: A decision support system for material and manufacturing process selection. Journal of Intelligent Manufacturing, vol. 9(3), pp. 265–276, ISSN 1572–8145, http://dx.doi.org/10.1023/A:1008866732609.

Halevi G.,Wang K.: Knowledge based manufacturing system (KBMS). Journal of Intelligent Manufacturing, vol. 18(4), pp. 467–474, 2007, ISSN 1572–8145, http://dx.doi.org/10.1007/s10845-007-0049-1.

Król D., SŁota R., Rauch Ł., Kitowski J., Pietrzyk M.: Harnessing Heterogeneous Computational Infrastructures for Studying Metallurgical Rolling Processes. pp. 1–9, 2014.

Kuziak R., Pietrzyk M.: Interpretation of SICO Test. pp. 39–44, 2002.

Kuziak R., Zalecki W., Szeliga D., Pietrzyk M.: Problem zastosowania symulacji fizycznych do identyfikacji modeli materiaŁów. Mechanik, vol. 87, 2014.

Madej Ł., Rauch Ł., Pietrzyk M.: Hybrid knowledge system for optimization of rolling process of DP steels. vol. 38, pp. 475–482, 2010.

Mahl A., Krikler R.: Approach for a rule based system for capturing and usage of knowledge in the manufacturing industry. Journal of Intelligent Manufacturing, vol. 18(4), pp. 519–526, 2007, ISSN 1572–8145, http://dx.doi.org/10.1007/s10845-007-0057-1.

McKay K., Black G.: The evolution of a production planning system: A 10–year case study. Computers in Industry, vol. 58(8-9), pp. 756–771, 2007, ISSN 0166–3615, http://www.sciencedirect.com/science/article/pii/S0166361507000206.

Ordon J., Kuziak R., Pietrzyk M.: History dependent constitutive law for austenitic steels. pp. 747–753, 2000, proc. Metal Forming 2000, eds, Pietrzyk M., Kusiak J., Majta J., Hartley P. and Pillinger I., Publ. A. Balkema.

Pietrzyk M., Kuziak R.: Modelling phase transformations in steel. pp. 145–179, 2012.

Pietrzyk M., Madej Ł., Szeliga D., Kuziak R., Pidvysotskyy V., Paul H., Wajda W.: Rheological Models of Metallic Materials. pp. 325–346, 2006, materials, Research in Polish Metallurgy at the Beginning of XXI Century, ed., wiztkowski K., Komitet Metalurgii PAN.

Rauch Ł.: Hybrid computer system for the design of flat rolling technology case study for multiphase steel. Computer Methods in Materials Science, vol. 12(3), pp. 218–224, 2012.

Rauch L., Front M., Bigaj M., Madej L.: Global Perspective for Competitive Enterprise, Economy and Ecology: Proceedings of the 16th ISPE International Conference on Concurrent Engineering, chap. Hybrid System Supporting Flexible Design of Flat Rolling Production Processes in Collaborative Environment, pp. 61–69. Springer London, London, 2009, ISBN 978-1-84882-762-2, http://dx.doi.org/10.1007/978-1-84882-762-2_6.

Rauch Ł., Gołąb R., Kuziak R., Pidvysots´kyy V., Pietrzyk M.: Zastosowanie komputerowego systemu eksperckiego do projektowania technologii walcowania na gorąco blach w LPS. vol. 1, pp. 104–109, 2012, prace Instytutu Metalurgii żelaza (in Polish).

Rauch Ł., Kuziak R., Gierulski B., Pietrzyk M.: Hybrydowe systemy wspomagania projektowania technologii przetwórstwa metali. vol. 62(1), pp. 142–149, 2010, prace Instytutu Metalurgii żelaza.

Rauch Ł., Madej Ł., Kuziak R., Matuszyk P., Pietrzyk M., Gierulski B., Chochorowski A.: Hybrydowy system wspomagania projektowania technologii produkcji tam szerokich walcowanych na gorzco w ArcelorMittal. Hutnik. Wiadomoci Hutnicze, vol. 76, pp. 290–296, 2009.

Rauch Ł., Madej Ł., Pietrzyk M.: Hybrid system for modeling and optimization of production chain in metal forming. Journal of Machine Engineering, vol. 8, pp. 14–22, 2008.

Rauch Ł., Skiba M., Kusiak J.: Computer system dedicated to optimization of production processes and cycles in metal forming industry. Computer Methods in Materials Science, vol. 14(1), pp. 3–12, 2014.

Schey J.: Friction effects in metalworking processes. pp. 17–81, 1970.

Sellars C.: Physical metallurgy of hot working. pp. 3–15, 1979.

Szeliga D., Gawad J., Pietrzyk M.: Inverse analysis for identification of rheological and friction models in metal forming. Computer Methods in Applied Mechanics and Engineering, vol. 195(48–49), pp. 6778–6798, 2006.

Sztangret Ł., Szeliga D., Kusiak J., Pietrzyk M.: Application of inverse analysis with metamodelling for identification of metal flowstress. vol. 51, pp. 440–446, 2012.

The Finite Element Method: Its Basis and Fundamentals. In: O. Zienkiewicz, R. Taylor, J. Zhu, eds., The Finite Element Method: its Basis and Fundamentals (Seventh Edition), pp. i –, Butterworth-Heinemann, Oxford, seventh edition ed., 2013, ISBN 978-1-85617-633-0, http://www.sciencedirect.com/science/article/pii/B9781856176330000198.

Trbacz L., Pietrzyk M.: Identification of the ductile fracture criterion on the basis of experimental data. vol. 53, pp. 469–477, 2014.