naa a22 4500 510790895 CHVBK 20180411083319.0 cr unu---uuuuu 180411e20131201xx s 000 0 eng 10.1007/s12289-012-1096-5 doi (NATIONALLICENCE)springer-10.1007/s12289-012-1096-5 Development of two-phase neural network-genetic algorithm hybrid model in modeling damage evolution in roll forming of aluminum sheet [Elektronische Daten] [Hong-Seok Park, Tran-Viet Anh] In this paper a two-phase artificial neural network-genetic algorithm (ANN-GA) hybrid model has been developed for the modeling and prediction of the damage evolution in the roll forming (RF) process of aluminum sheet metal, as a function of process parameters. The multilayer perceptron is used to build the network while the genetic algorithm (GA) is employed to optimize the network structure in the modeling phase. In detail, the number of hidden layer, hidden neurons, weights and biases of the network are optimized by GA to minimize the error between predicted values and actual results. After the modeling phase the optimization of parameters is carried out in the optimization phase to minimize the damage in the aluminum sheet during the forming process. In this work the experimental data used for training and verifying the network is obtained automatically by the integration between CAD-CAE. As a result, the predicted results are validated with the actual values and a good agreement is observed. Moreover, the parametric study also is performed to find the relative influences of process parameters on the damage evolution. It is proven that the hybrid model is the powerful tool for modeling and predicting such a highly nonlinear problem as the damage evolution in RF process. The developed two-phase ANN - GA hybrid model is a new approach that can bring benefits to the forming industry by predicting and preventing the failure at the design stage, as well as improving efficiently the product's quality by optimizing the process parameters. Springer-Verlag France, 2012 ANN-GA hybrid model nationallicence Roll forming nationallicence Damage evolution nationallicence Park Hong-Seok School of Mechanical and Automotive engineering, University of Ulsan, San 29, Mugeo 2-dong, Namgu, Ulsan, P.O. Box 18, 680-749, Ulsan, Korea aut Anh Tran-Viet School of Mechanical and Automotive engineering, University of Ulsan, San 29, Mugeo 2-dong, Namgu, Ulsan, P.O. Box 18, 680-749, Ulsan, Korea aut International Journal of Material Forming Springer Paris 6/4(2013-12-01), 423-436 1960-6206 6:4<423 2013 6 12289 https://doi.org/10.1007/s12289-012-1096-5 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s12289-012-1096-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Park Hong-Seok School of Mechanical and Automotive engineering, University of Ulsan, San 29, Mugeo 2-dong, Namgu, Ulsan, P.O. Box 18, 680-749, Ulsan, Korea aut NATIONALLICENCE 700 1- Anh Tran-Viet School of Mechanical and Automotive engineering, University of Ulsan, San 29, Mugeo 2-dong, Namgu, Ulsan, P.O. Box 18, 680-749, Ulsan, Korea aut NATIONALLICENCE 773 0- International Journal of Material Forming Springer Paris 6/4(2013-12-01), 423-436 1960-6206 6:4<423 2013 6 12289 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer