Emulation and Prediction of the Cold Roll Forming Force

2012 ◽  
Vol 472-475 ◽  
pp. 206-213 ◽  
Author(s):  
Jin Gang Wu ◽  
Qiang Li ◽  
Yu Yan

In order to optimize the craft parameter of cold roll forming, this paper puts forward a new method reflect the law between cold roll forming force and craft parameter. The method combines artificial neural network and finite element emulation for cold roll forming, and begins with analyzing the cold roll forming to decide the main factors that have an impact on roll forming force. According to the single factor experiment method, the paper establishes the roll forming emulation model to simulate, and a BP neural network is constructed with the sample data from the simulation.

1964 ◽  
Vol 7 (28) ◽  
pp. 827-834 ◽  
Author(s):  
Moriji MASUDA ◽  
Tadao MUROTA ◽  
Takashi JIMMA ◽  
Toshitaka TAMANO ◽  
Toshiyuki AMAGAI

1996 ◽  
Vol 59 (1-2) ◽  
pp. 41-48 ◽  
Author(s):  
Nitin Duggal ◽  
Mustafa A. Ahmetoglu ◽  
Gary L. Kinzel ◽  
Taylan Altan

Author(s):  
Chang Guo ◽  
Ming Gao ◽  
Peixin Dong ◽  
Yuetao Shi ◽  
Fengzhong Sun

As one kind of serious environmental problems, flow-induced noise in centrifugal pumps pollutes the working circumstance and deteriorates the performance of pumps, meanwhile, it always changes drastically under various working conditions. Consequently, it is extremely significant to predict flow-induced noise of centrifugal pumps under various working conditions with a practical mathematical model. In this paper, a three-layer back propagation (BP) neural network model is established and the number of input, hidden and output layer node is set as 3, 6 and 1, respectively. To be specific, the flow rate, rotational speed and medium temperature are chosen as input layer, and the corresponding flow-induced noise evaluated by average of total sound pressure level (A_TSPL) as output layer. Furthermore, the tansig function is used to act as transfer function between the input layer and hidden layer, and the purelin function is used between hidden layer and output layer. The trainlm function based on Levenberg-Marquardt algorithm is selected as the training function. By using a large number of sample data, the training of the network model and prediction research are accomplished. The results indicate that good correlation is established among the sample data, and the predictive values show great consistence with simulation ones, of which the average relative error of A_TSPL in process of verification is 0.52%. The precision of the model can satisfy the requirement of relevant research and engineering application.


2018 ◽  
Vol 101 (1-4) ◽  
pp. 181-194 ◽  
Author(s):  
H. Mohammdi Najafabadi ◽  
H. Moslemi Naeini ◽  
R. Safdarian ◽  
M. M. Kasaei ◽  
D. Akbari ◽  
...  

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