Experimental investigation and modeling of Fiber Metal Laminates hydroforming process by the GWO optimized neuro-fuzzy network

Rabiee, Amir Hossein; Sherkatghanad, Ehsan; Zeinolabedin Beygi, Ali; Moslemi Naeini, Hassan; Lang, Lihui

doi:10.22061/jcarme.2022.8268.2101

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	Experimental investigation and modeling of Fiber Metal Laminates hydroforming process by the GWO optimized neuro-fuzzy network
Journal of Computational & Applied Research in Mechanical Engineering (JCARME)
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 20 اردیبهشت 1401 اصل مقاله (1.35 MB)
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) are 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 have been utilized for training and testing of the ANFIS. The accurateness of the attained network has been 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 has been 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, it can be seen that the root means square error of aluminum thinning rate (upper layer and lower layer), the height of wrinkles, and achieved depth respectively) of the test section, are 1.67, 2.25, 0.05, and 2.67. 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

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Experimental investigation and modeling of Fiber Metal Laminates hydroforming process by the GWO optimized neuro-fuzzy network