Amir Hossein Rabiee - Department of Mechanical Engineering, Arak University of Technology, Arak, Iran
Ehsan Sherkatghanad - Department of Mechanical Engineering, Tarbiat Modares University, Tehran, Iran
Ali Zeinolabedin Beygi - Department of Mechanical Engineering, Tarbiat Modares University, Tehran, Iran
Hassan Moslemi Naeini - Department of Mechanical Engineering, Faculty of Engineering, Tarbiat Modares University, P.O.Box ۱۴۱۱۵/۱۴۳, Tehran, I.R. Iran
Lihui Lang - Department of Industrial and Manufacturing System Engineering, Beihang University, Beijing, China

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 ۱.۶۷, ۲.۲۵, ۰.۰۵, and ۲.۶۷, respectively. It is also observed that the correlation coefficient for the test data is very close to ۱, which demonstrates the high precision of the ANFIS in predicting the outputs of the hydroforming procedure.

Neural Network, ANFIS, Gray wolf algorithm, Hydroforming, Fiber metal laminates