Verification of application of ANN modelling in study of compressive behaviour of aluminium sponges

• Autorzy: Stręk Anna M. , Dudzik Marek , Kwiecień Arkadiusz , Wańczyk Krzysztof , Lipowska Barbara

Identyfikatory

DOI

10.24423/EngTrans.991.20190615

• Języki publikacji: EN

Abstrakty

EN

This article presents a preliminary neural network analysis of the compressive behaviour of aluminium open-cell sponges and answers the question of whether this phenomenon can be modelled using artificial intelligence. The research consisted of two phases: first – compression experiments, which in turn provided data for the second phase – the artificial neural network (ANN) analysis. A two-argument function was proposed and tested using the gathered experimental data with a two-layer feedforward network. The determination coefficient R 2 for linear correlation between targets and modelling outputs was chosen as the criterion for the assessment of the quality of modelling. The obtained values were R 2 > 0.96, which shows that neural networks hold the capacity to address the characterisation of the mechanical response of aluminium open-cell sponges in compression. Additionally, the mean absolute relative error (MARE) and the mean square error (MSE) were also determined.

Słowa kluczowe

EN

metal sponges; aluminium sponges; compression tests; artificial neural networks

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Engineering Transactions
• Rocznik: 2019
• Tom: Vol. 67, iss. 2
• Strony: 271--288
• Opis fizyczny: Bibliogr. 45 poz., rys., tab., wykr.

Twórcy

autor

Stręk Anna M.

• Cracow University of Technology Faculty of Civil Engineering Institute of Structural Mechanics Warszawska 24, 31-155 Kraków, Poland

autor

Dudzik Marek

• Cracow University of Technology Faculty of Electrical and Computer Engineering Department of Traction and Traffic Control Warszawska 24, 31-155 Kraków, Poland

autor

Kwiecień Arkadiusz

• Cracow University of Technology Faculty of Civil Engineering Institute of Structural Mechanics Warszawska 24, 31-155 Kraków, Poland

autor

Wańczyk Krzysztof

• Foundry Research Institute Center for Design and Prototyping Zakopiańska 73, 30-418 Kraków, Poland

autor

Lipowska Barbara

• Institute of Ceramics and Building Materials Refractory Materials Division in Gliwice Toszecka 99, 44-100 Gliwice, Poland

Bibliografia

• 1. Degischer H., Kriszt B., Handbook of cellular metals: production, processing, applications, Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, 2002.
• 2. García-Moreno F., Commercial applications of metal foams: their properties and production, Materials, 9(2): 85–111, 2016.
• 3. Sobczak J., Wojciechowski A., Boyko L., Drenchev L., Darłak P., Dudek P., Highly porous materials [in Polish: Materiały wysokoporowate], Instytut Odlewnictwa, Kraków, 2005.
• 4. Simancik F., Jerz J., Kováčik J., Minár P., Aluminium foam – a new light-weight structural material, Kovové Materiály (Metallic Materials), 35: 265–277, 1997.
• 5. Duocel r copper foam, ERG Materials and Aerospace Corp., Oakland, CA 94608, US, https://ergaerospace.com/materials/duocel-copper-foam (accessed 15 Oct. 2018).
• 6. Dunand D.C., Processing of titanium foams, Advanced Engineering Materials, 6(6): 369– 376, 2004.
• 7. Jakubowicz J., Adamek G., Dewidar M., Titanium foam made with saccharose as a space holder, Journal of Porous Materials, 20(5): 1137–1141, 2013.
• 8. Banhart J., Gold and gold alloy foams, Gold Bulletin, 41(3): 251–256, 2008.
• 9. Smith B.H., Szyniszewski S., Hajjar J.F., Schafer B.W., Arwade S.R., Steel foam for structures: A review of applications, manufacturing and material properties, Journal of Constructional Steel Research, 71: 1–10, 2012.
• 10. Duarte I., Oliveira M., Aluminium alloy foams: production and properties, [in:] K. Kondoh [Ed.], Powder Metallurgy, InTech, pp. 47–72, 2012.
• 11. Nieh T.G., Higashi K., Wadsworth J., Effect of cell morphology on the compressive properties of open-cell aluminum foams, Materials Science and Engineering: A, 283(1–2): 105–110, 2000.
• 12. Miedzińska D., Niezgoda T., Gieleta R., Numerical and experimental aluminum foam microstructure testing with the use of computed tomography, Computational Materials Science, 64: 90–95, 2012.
• 13. Zhou J., Shrotriya P., Soboyejo W.O., Mechanisms and mechanics of compressive deformation in open-cell Al foams, Mechanics of Materials, 36(8): 781–797, 2004.
• 14. Papadopoulos D.P., Omar H., Stergioudi F., Tsipas S.A., Lefakis H., Michailidis N., A novel method for producing Al-foams and evaluation of their compression behavior, Journal od Porous Materials, 17(6): 773–777, 2010.
• 15. Wicklein M., Thoma K., Numerical investigations of the elastic and plastic behaviour of an open-cell aluminium foam, Materials Science and Engineering: A, 397(1–2): 391–399, 2005.
• 16. Robertson I.M. et al., Towards an integrated materials characterization toolbox, Journal of Material Research, 26(1): 1341–1383, 2011.
• 17. Kurzyński M., Methods of artificial intelligence for engineers [in Polish: Metody sztucznej inteligencji dla inżynierów], Państwowa Wyższa Szkoła Zawodowa im. Witelona w Legnicy, Stowarzyszenie „Wspólnota Akademicka”, Legnica, 2008.
• 18. Lefik M., Application of artificial neural networks in mechanics and engineering [in Polish: Zastosowanie sztucznych sieci neuronowych w mechanice i w inżynierii], Wydawnictwo Politechniki Łódzkiej, Łódź, 2005.
• 19. Jakubek M., Application of artificial neural networks in selected problems of experimental mechanics and structural engineering [in Polish: Zastosowanie sztucznych sieci neuronowych w wybranych zagadnieniach eksperymentalnej mechaniki materiałów i konstrukcji], PhD thesis, Politechnika Krakowska, Kraków, 2007.
• 20. Pietrzyk M., Kusiak J., Szeliga D., Rauch Ł., Sztangret Ł., Górecki G., Application of metamodels to identification of metallic materials models, Advances in Materials Science and Engineering, 2016, article ID: 2357534, 20 pages, 2016.
• 21. Stręk A.M., Assessment of strength and functional properties of cellular materials [in Polish: Ocena właściwości wytrzymałościowych i funkcjonalnych materiałów komórkowych], PhD thesis, AGH, Kraków, 2017.
• 22. Kasza P., Lipowska B., Pęcherski R.B., Stręk A.M., Wańczyk K., Compression of open-cell aluminium, Engineering Transactions, 64(4): 629–634, 2016.
• 23. Stręk A.M., Lipowska B., Wańczyk K., Selected aspects of manufacturing of aluminium sponge, Archives of Metallurgy and Materials, 64(3), 2019 (accepted, in print).
• 24. Banhart J., Manufacture, characterisation and application of cellular metals and metal foams, Progress in Materials Science, 46(6): 559–632, 2001.
• 25. Kränzlin N., Niederberger M., Controlled fabrication of porous metals from the nanometer to the macroscopic scale, Materials Horizons, 2: 359–377, 2015.
• 26. Kanaun S., Tkachenko O., Representative volume element and effective elastic properties of open cell foam materials with random microstructures, Journal of Mechanics of Materials and Structures, 2(8): 1607–1628, 2007.
• 27. ISO 13314:2011, Mechanical testing of metals – Ductility testing – Compression test for porous and cellular metals, International Organization for Testing, 2011.
• 28. Jang W.-Y., Kraynik A.M., Kyriakides S., On the microstructure of open-cell foams and its effect on elastic properties, International Journal of Solids and Structures, 45(7–8): 1845–1875, 2008.
• 29. Gibson I.J., Ashby M.F., The mechanics of three-dimensional cellular materials, Proceedings of the Royal Society A, 382(1782): 43–59, 1982.
• 30. Dudzik M., Mielnik R., Wróbel Z., Preliminary analysis of the effectiveness of the use of artificial neural networks for modelling time-voltage and time-current signals of the combination wave generator, [in:] SPEEDAM 2018 – Proceedings: International Symposium on Power Electronics, Electrical Drives, Automation and Motion, 8445277, pp. 1095–1100, 2018.
• 31. Dudzik M., Tomczyk K., Jagiełło A.S., Analysis of the error generated by the voltage output accelerometer using the optimal structure of an artificial neural network, [in:] Proceedings of the 19th International Conference on Research and Education in Mechatronics, REM 2018, 8421789, pp. 7–11, 2018.
• 32. Dudzik M., Mielnik R., Wróbel Z., Preliminary analysis of the effectiveness of the use of artificial neural networks for modeling time-voltage signal of the combination wave generator, 18th International Symposium on Electromagnetic Fields in Mechatronics, Electrical and Electronic Engineering, ISEF 2017, 8090698, 2017.
• 33. McCaffrey J., How to standardize data for neural networks, Visual Studio Magazine 01/15/2014, https://visualstudiomagazine.com/articles/2014/01/01/how-to-standardizedata-for-neural-networks.aspx (accessed 15 Sept 2018).
• 34. Demuth H., Beale M., Hagan M., Neural Network Toolbox 5 User’s Guide, The MathWorks Inc., 2007.
• 35. Mathworks documentation: mapminmax, https://www.mathworks.com/help/deeplearning/ref/mapminmax.html (accessed 21 Feb 2019).
• 36. Matlab and automatic target normalization: mapminmax. Don’t trust your Matlab framework!, https://neuralsniffer.wordpress.com/2010/10/17/matlab-and-automatic-target-normalization-mapminmax-donttrust-your-matlab-framework/ (accessed 21st Feb 2019).
• 37. Hagan M.T., Demuth H.B., Beale M.H., De Jesus O., Neural network design, 2nd Ed., eBook, 2014.
• 38. Levenberg K., A method for the solution of certain non-linear problems in least squares, Quarterly of Applied Mathematics, 2: 164–168, 1944.
• 39. Marquardt D.W., An algorithm for least-squares estimation of nonlinear parameters, SIAM Journal on Applied Mathematics, 11(2): 431–441, 1963.
• 40. Madsen K., Nielsen H.B., Tingleff O., Methods for non-linear least squares problems, Informatics and Mathematical Modelling Technical University of Denmark, 2nd Ed., April 2004, http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/ 3215/pdf/imm3215.pdf (accessed 15 Oct 2018).
• 41. Levenberg-Marquardt algorithm, article in Wikipedia, on-line: https://en.wikipedia.org/ wiki/Levenberg-Marquardt_algorithm (accessed 15 Oct 2018).
• 42. Girard A., Excerpt from theoretical and instrumental optics review [in French: Excerpt from Revue d’optique théorique et instrumentale], Revue d’Optique, 37: 225–241, 397–424, 1958.
• 43. Wynne C.G., Lens designing by electronic digital computer: I, Proceedings of the Physical Society of London, 73(5): 777–787, 1959.
• 44. Morrison D.D., Methods for nonlinear least squares problems and convergence proofs, [in:] Proceedings of the Jet Propulsion Laboratory Seminar on Tracking Programs and Orbit Determination, Pasadena, California, pp. 1–9, 1960.
• 45. Dudzik M., Stręk A.M., ANN architecture specifications for modelling of open-cell aluminium under compression, 2019 (in preparation).

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).