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Predicting the strength of adhesive joints using neural networks
Języki publikacji
Abstrakty
This paper presents the results of the analysis of suitability of artificial intelligence for processing of experimental information related to strength of adhesive joints. The efficiency of neuron artificial network was compared with the efficiency of typical methods of statistical analysis such as linear and polynomial regress. The research was conducted, based on a statical, determinated multifactorial program. The list of arguments comprised length of overlap, thickness of adhesive layer, thickness of joined materials, geometrical parameters of surface. The output result was the load capacity as a force needed to destruct an adhesive joint. The research was done in the situation, when other parameters affecting the strength of adhesive joint were stable. The surface answers of network for entrance parameters combination for the defined nodal point, presented in this paper, indicate non-linear influence of testing parameters on adhesive joints strength. The influence of average square ordinates of roughness profile is close to linear. However, its influence changes following the change of other parameters. The presented graphs show, that the highest adhesive joints strength is a function of complicated relations between the geometrical parameters of joints. The example results presented in this paper, enable to state that the artificial neuron network, thanks to the credibility of modeling and predicting the strength of adhesive joints, can serve as a base of knowledge for constructors and technologists, using adhesive joints in designed constructions. These networks can significantly commit to the increased quality of products and decrease the construction cost.
Czasopismo
Rocznik
Tom
Strony
41-47
Opis fizyczny
Bibliogr. 23 poz., rys., tab.
Twórcy
autor
- Politechnika Lubelska
Bibliografia
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- 5. Kinloch A. J.: Adhesives in engineering. Proc. Instn. Mech. Engrs. Vol. 211, Part G. 1997, pp. 307-335.
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- 8. Gengkai Нu: Mixed mode fracture analysis of adhesive lap joints. Composites Engineering. Vol. 5, No. 8, pp. 1043-1050, 1995.
- 9. Kum Cheol Shin, Jung Ju Lee, Dai Gil Lee: A study on the lap shear strength of a co-cured single lap joint. J. Adhesion Sci. Technol., Vol. 14, No. 1, pp 123-139 (2000).
- 10. Erol Sancaktar, Sean R. Simmons: Optimization of adhesively-bonded single lap joints by adherend notching. J. Adhesion Sci. Technol., Vol. 14, No. 11, pp. 1363-1404 (2000).
- 11. Erol Sancaktar, Sumeet Kumar. Selective use of rubber toughening to optimize lap-joint strength. J Adhesion Sci. Technol., Vol. 14, No. 10, pp. 1265-1296 (2000).
- 12. J. Domińczuk: Design basis of surface layer energetic states. The 4-th International Scientific Conference - Development of metal cutting DMC 2002, pp. 91 -94.
- 13. Kuczmaszewski J.: Fundamentals of metal-metal adhesive joint design. Lublin University of Technology: Polish Academy of Sciences, Lublin Branch, 2006.
- 14. K. Sławińska: Wyroby epoksydowe z Zakładów Chemicznych "Organika - Sarzyna", "Polimery", nr 11-12 (43)/1998 r., s. 741 -746.
- 15. PN-EN 1465:2003: Kleje. Oznaczanie wytrzymałości na ścinanie przy rozciąganiu połączeń na zakładkę materiału sztywnego ze sztywnym.
- 16. Carling Α.: Introducing Neural Networks. Wilmslow, UK: Sigma Press (1992).
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- 18. D. Patterson: Artificial Neural Networks. Singapore: Prentice Hall (1996).
- 19. Fausett L.: Fundamentals of Neural Networks. New York: Prentice Hall (1994).
- 20. Domińczuk J:. Synteza wpływu wybranych czynników konstrukcyjnych i technologicznych na wytrzymałość połączeń klejowych. Przegląd Mechaniczny 6'06, s. 23-28.
- 21. Kuczmaszewski J:. Podstawy konstrukcyjne i technologiczne oceny wytrzymałości adhezyjnych połączeń metali. WU Politechnika Lubelska, 1995.
- 22. Domińczuk J., Kuczmaszewski J.: Modelling of adhesive joints and predicting their strength with the use of neural networks. ELSEVIER. Computational Materials Science, Vol. 43 (2008), pp. 165 - 170.
- 23. Godzimirski J.: Wytrzymałość doraźna konstrukcyjnych połączeń klejowych. WNT, Warszawa 2002.
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-article-BOS5-0025-0031