naa a22 4500 510823084 CHVBK 20180411083543.0 cr unu---uuuuu 180411e20131201xx s 000 0 eng 10.1007/s12541-013-0288-3 doi (NATIONALLICENCE)springer-10.1007/s12541-013-0288-3 Optimization of roll forming process with evolutionary algorithm for green product [Elektronische Daten] [Hong Park, Trung Nguyen] Knowledge-Based Neural Network model is known as one of the most useful methods which can predict every single variability to create the process parameters for the data on Roll Forming process. To get the best quality of product and process parameters in roll forming, the Knowledge-Based Neural Network has to be trained with high reliability. To obtain the target aimed, this paper proposes a new novel of the optimal algorithm for training in the Knowledge-Based Neural Network model with the integration between Genetic Algorithm and Hill Climbing Algorithm. Initially, a global optimization method is carried out to find the global optimum area by using Genetic Algorithm, and then the Hill climbing Algorithm will effectively detect the positions of that local optimal region with high accuracy in the training of the Knowledge-Based Neural Network model. Additionally, to obtain the trained data set of the Knowledge-Based Neural Network model, the Finite Element Analysis result of the high fidelity Finite Element Model is used. From the results of simulation, we can find out that the efficiency of the proposed method is higher than the conventional methods in optimization of the roll forming process. Korean Society for Precision Engineering and Springer-Verlag Berlin Heidelberg, 2013 Roll forming process nationallicence Knowledge-based neural network nationallicence Hill climbing nationallicence Genetic algorithm nationallicence d : inner distance between roll stands (mm) nationallicence ω : rotation velocity of rolls (rad/s) nationallicence f : friction coefficient nationallicence r : ratio between the roll gap and sheet thickness nationallicence D : damage variable nationallicence α : spring back angle nationallicence Park Hong School of Mechanical Engineering, University of Ulsan, 93 Daehak-ro, Nam-gu, 680-749, Ulsan, South-Korea aut Nguyen Trung School of Mechanical Engineering, University of Ulsan, 93 Daehak-ro, Nam-gu, 680-749, Ulsan, South-Korea aut International Journal of Precision Engineering and Manufacturing Springer Berlin Heidelberg 14/12(2013-12-01), 2127-2135 2234-7593 14:12<2127 2013 14 12541 https://doi.org/10.1007/s12541-013-0288-3 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s12541-013-0288-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Park Hong School of Mechanical Engineering, University of Ulsan, 93 Daehak-ro, Nam-gu, 680-749, Ulsan, South-Korea aut NATIONALLICENCE 700 1- Nguyen Trung School of Mechanical Engineering, University of Ulsan, 93 Daehak-ro, Nam-gu, 680-749, Ulsan, South-Korea aut NATIONALLICENCE 773 0- International Journal of Precision Engineering and Manufacturing Springer Berlin Heidelberg 14/12(2013-12-01), 2127-2135 2234-7593 14:12<2127 2013 14 12541 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer