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Rabiee, Amir Hossein, Sherkatghanad, Ehsan, Zeinolabedin Beygi, Ali, Moslemi Naeini, Hassan, Lang, Lihui. (1401). Experimental investigation and modeling of fiber metal laminates hydroforming process by GWO optimized neuro-fuzzy network. فناوری آموزش, 12(2), 193-209. doi: 10.22061/jcarme.2022.8268.2101
Amir Hossein Rabiee; Ehsan Sherkatghanad; Ali Zeinolabedin Beygi; Hassan Moslemi Naeini; Lihui Lang. "Experimental investigation and modeling of fiber metal laminates hydroforming process by GWO optimized neuro-fuzzy network". فناوری آموزش, 12, 2, 1401, 193-209. doi: 10.22061/jcarme.2022.8268.2101
Rabiee, Amir Hossein, Sherkatghanad, Ehsan, Zeinolabedin Beygi, Ali, Moslemi Naeini, Hassan, Lang, Lihui. (1401). 'Experimental investigation and modeling of fiber metal laminates hydroforming process by GWO optimized neuro-fuzzy network', فناوری آموزش, 12(2), pp. 193-209. doi: 10.22061/jcarme.2022.8268.2101
Rabiee, Amir Hossein, Sherkatghanad, Ehsan, Zeinolabedin Beygi, Ali, Moslemi Naeini, Hassan, Lang, Lihui. Experimental investigation and modeling of fiber metal laminates hydroforming process by GWO optimized neuro-fuzzy network. فناوری آموزش, 1401; 12(2): 193-209. doi: 10.22061/jcarme.2022.8268.2101

Experimental investigation and modeling of fiber metal laminates hydroforming process by GWO optimized neuro-fuzzy network

Journal of Computational & Applied Research in Mechanical Engineering (JCARME)
مقاله 5، دوره 12، شماره 2 - شماره پیاپی 24، اردیبهشت 2023، صفحه 193-209    اصل مقاله (1.24 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22061/jcarme.2022.8268.2101
نویسندگان
Amir Hossein Rabiee1؛ Ehsan Sherkatghanad2؛ Ali Zeinolabedin Beygi2؛ Hassan Moslemi Naeini* 3؛ Lihui Lang4
1Department of Mechanical Engineering, Arak University of Technology, Arak, Iran
2Department of Mechanical Engineering, Tarbiat Modares University, Tehran, Iran
3Department of Mechanical Engineering, Faculty of Engineering, Tarbiat Modares University, P.O.Box 14115/143, Tehran, I.R. Iran
4Department of Industrial and Manufacturing System Engineering, Beihang University, Beijing, China
تاریخ دریافت: 31 تیر 1400،  تاریخ بازنگری: 18 اردیبهشت 1401،  تاریخ پذیرش: 20 اردیبهشت 1401 
چکیده
In this paper, by considering the processing parameters, including blank holder force, blank holder gap, and cavity pressure as the most important input factors in the hydroforming process, an experimental design is performed, and an adaptive neural-fuzzy inference system (ANFIS) is applied to model and predict the behavior of aluminum thinning rate (upper layer and lower layer), the height of wrinkles and achieved depths that are extracted in hydroforming process. Also, the optimal constraints of the network structure are obtained by the gray wolf optimization algorithm. Accordingly, the results of experimental tests are utilized for training and testing of the ANFIS. The accurateness of the attained network is examined using graphs and also based on the statistical criteria of root mean square error, mean absolute error, and correlation coefficient. The results show that the attained model is very effective in approximating the aluminum thinning rate (upper layer and lower layer), the height of wrinkles, and achieved depth in the hydroforming process. Finally, the results also show that the root means of the square error of aluminum thinning rate (upper layer and lower layer), the height of wrinkles, and achieved depth of the test section are 1.67, 2.25, 0.05, and 2.67, respectively. It is also observed that the correlation coefficient for the test data is very close to 1, which demonstrates the high precision of the ANFIS in predicting the outputs of the hydroforming procedure.
کلیدواژه‌ها
Neural network؛ ANFIS؛ Gray wolf algorithm؛ Hydroforming؛ Fiber metal laminates
مراجع
[1] E.C. Botelho, R.A. Silva, L.C. Pardini and M.C. Rezende, “A review on the development and properties of continuous fiber/epoxy/aluminum hybrid composites for aircraft structures”, Mater. Res., Vol. 9, No. 3, pp. 247-256, (2006).

[2] T. Sinmazçelik, E. Avcu, M.Ö. Bora and O. Çoban, “A review: Fibre metal laminates, background, bonding types and applied test methods”, Mater. Des., Vol. 32, No. 7, pp. 3671-3685, (2011).

[3] L.B. Vogelesang and A. Vlot, “Development of fibre metal laminates for advanced aerospace structures”, J. Mater. Process. Technol., Vol. 103, No. 1, pp 1-5, (2000).

[4] T. Takamatsu, T. Matsumura, N. Ogura, T. Shimokawa and Y. Kakuta, “Fatigue crack growth properties of a GLARE3-5/4 fiber/metal laminate”, Eng. Fract. Mech., Vol. 63, No. 3, pp. 253-272, (1999).

[5] J. Carrillo and W. Cantwell, “Mechanical properties of a novel fiber–metal laminate based on a polypropylene composite”, Mech. Mater., Vol. 41, No. 7, pp. 828-838, (2009).

[6] T. Takamatsu, T. Shimokawa, T. Matsumura, Y. Miyoshi and Y. Tanabe, “Evaluation of fatigue crack growth behavior of GLARE3 fiber/metal laminates using a compliance method”, Eng. Fract. Mech., Vol. 70, No. 18, pp. 2603-2616, (2003).

[7] J. Homan, “Fatigue initiation in fibre metal laminates”, In.t J. Fatigue., Vol. 28, No. 4, pp. 366-374, (2006).

[8] A. Volt, L. Vogelesang and T. De Vries, “Towards application of fiber metal laminates in larger aircraft”, Aircr. Eng. Aerosp. Technol., Vol. 71, No. 6, pp. 558-570, (1999).

[9] S. Suresh and V.S.S. Kumar, “Investigation on influence of stamp forming parameters on formability of thermoplastic composite”, Ass. Bra. Pol., Vol. 28, No. 5, pp. 422-432,  (2018).

[10] H. Friedrich and S. Schumann, “Research for a new age of magnesium in the automotive industry” J. Mater. Process. Technol., Vol. 117, No. 3, pp. 276-281, (2001).

[11] H. Kang and G. Reyes-Villanueva, “Fatigue prediction of lightweight thermoplastic fiber-metal laminates”, J. Test. Eval., Vol. 35, No. 3, pp. 266-71, (2007).

[12] R. Zhang, L. Lang, R. Zafar, L. Lin and W. Zhang, “Investigation into thinning and spring back of multilayer metal forming using hydro-mechanical deep drawing (HMDD) for lightweight parts”, Int. J. Adv. Manuf. Technol., Vol. 82, No 5-8, pp. 817-826, (2016).

[13] R. Zhang, L. Lang and R. Zafar, “FEM-based strain analysis study for multilayer sheet forming process”, Front. Mech. Eng., Vol. 10, No. 4, pp. 373-379, (2015).

[14] R.-j. Zhang, L.-h. Lang, R. Zafar, L. Kui and W. Lei, “Effect of gap generator blank thickness on formability in multilayer stamp forming process”, Trans. Nonferrous. Met. Soc. China., Vol. 26, No. 9, pp. 2442-2448, (2016).

[15] L.C. Chan and W.K.R. Kot, “Determination of loading paths in warm hydroforming reinforced quadrilateral tubular components”, Mater. Manuf. Process., Vol. 29, No. 1, pp.  32-36, (2014).

[16] R. Gerlach, C.R. Siviour, J. Wiegand and N. Petrinic, “The strain rate dependent material behavior of S-GFRP extracted from GLARE” Mech. Adv. Mater. Struct., Vol. 20, No. 7, pp.  505-514, (2013).

[17] Z.-J. Wang, L. Yi, J.-g. Liu and Y.-H. Zhang, “Evaluation of forming limit in viscous pressure forming of automotive aluminum alloy 6k21-T4 sheet”, Trans. Nonferrous. Met. Soc. China., Vol. 17, No. 6, pp. 1169-1174, (2007).

[18] J.P.M. Correia and S. Ahzi, “Electromagnetic sheet bulging: analysis of process parameters by FE simulations”, Key. Eng. Mater., Vol. 554-557, pp. 741-748, (2013).

[19] E. Sherkatghanad, L. Lang, S. Liu and Y. Wang. “Innovative approach to mass production of fiber metal laminate sheets”, Mater. Manuf. Process., Vol. 33, No. 5, pp. 552-563, (2018).

[20] M.M. Thomas, B. Joseph and J.L. Kardos “Experimental characterization of autoclave‐cured glass‐epoxy composite laminates: Cure cycle effects upon thickness, void content, and related phenomena”, Polym. Compos., Vol. 18, No. 3, pp. 283-299, (1997).

[21] J. Yanagimoto and K. Ikeuchi, “Sheet forming process of carbon fiber reinforced plastics for lightweight parts”, CIRP. Ann., Vol. 61, No. 1, pp. 247-250, (2012).

[22] T. Yokozeki, Y. Iwahori, S. Ishiwata and K. Enomoto, “Mechanical properties of CFRP laminates manufactured from unidirectional prepregs using CSCNT-dispersed epoxy Composites”, Compo.s Part A. Appl. Sci. Manuf., Vol. 38, No. 10, pp. 2121-2130, (2007).

[23] L. Mosse, P. Compston, W.J. Cantwell, M. Cardew-Hall and S. Kalyanasundaram, “Stamp forming of polypropylene based fibre–metal laminates: the effect of process variables on formability”, J. Mater. Process. Technol., Vol. 172, No. 2, pp. 163-168, (2006).

[24] L. Mosse, W.J. Cantwell, M. Cardew-Hall, P. Compston and S. Kalyanasundaram, “A study of the effect of process variables on the stamp forming of rectangular cups using Fibre-Metal Laminate systems”, Adv. Mat. Res., Vol. 6-8, pp. 649-656, (2005).

[25] M.R. Abdullah and W.J. Cantwell, “The high-velocity impact response of thermoplastic–matrix fibre–metal laminates”, J. Strain. Anal. Eng. Des., Vol. 98, No. 7,  pp. 432-443, (2012).

[26] A. Yaghoobi, M. Bakhshi-Jooybari, A. Gorji and H. Baseri, “Application of adaptive neuro fuzzy inference system and genetic algorithm for pressure path optimization in sheet hydroforming process”, Int. J. Adv. Manuf. Technol., Vol. 86, No. 9, pp.  2667-2677, (2016).

[27] S. Kumar, S. Dhanabalan and C. Narayanan, “Application of ANFIS and GRA for multi-objective optimization of optimal wire-EDM parameters while machining Ti–6Al–4V alloy”, SN. Appl. Sci., Vol. 1, No. 4, pp. 1-12, (2019).

[28] B. Podder, P. Banerjee, K.R. Kumar and N.B. Hui, “Development of ANFIS Model for Flow Forming of Solution Annealed H30 Aluminium Tubes”, Solid. State. Phenom., Vol. 261, pp. 378-385, (2017).

[29] Y.D. Asl, Y.Y. Woo, Y. Kim and Y.H. Moon, “Non-sorting multi-objective optimization of flexible roll forming using artificial neural networks”, Int. J. Adv. Manuf. Technol, Vol. 107, No. 5, pp. 2875-2888, (2020). 

[30] R.T. Esfahani, S.i. Golabi and Z. Zojaji, “Optimization of finite element model of laser forming in circular path using genetic algorithms and ANFIS”, Soft. Comput., Vol. 20, No. 5, pp. 2031-2045, (2016).

[31] K. Maji and G. Kumar, “Inverse analysis and multi-objective optimization of single-point incremental forming of AA5083 aluminum alloy sheet”, Soft. Comput., Vol. 24, No. 6, pp. 4505-4521, (2020).

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