Modelling the correlation between the geometrical features and the forming limit strains of perforated Al 8011 sheets using artificial neural network

2010 ◽  
Vol 4 (4) ◽  
pp. 389-399 ◽  
Author(s):  
K. Elangovan ◽  
C. Sathiya Narayanan ◽  
R. Narayanasamy
2016 ◽  
Vol 716 ◽  
pp. 770-778 ◽  
Author(s):  
Mohamed Mohamed ◽  
Sherif Elatriby ◽  
Zhusheng Shi ◽  
Jian Guo Lin

Warm stamping techniques have been employed to solve the formability problem in forming aluminium alloy panels. The formability of sheet metal is a crucial measure of its ability for forming complex-shaped panel components and is often evaluated by forming limit diagram (FLD). Although the forming limit is a simple tool to predict the formability of material, determining FLD experimentally at warm/hot forming condition is quite difficult. This paper presents the artificial neural network (ANN) modelling of the process based on experimental results (different temperature, 20°C-300°C and different forming rates, 5-300 mm.s-1) is introduced to predict FLDs. It is shown that the ANN can predict the FLDs at extreme conditions, which are out of the defined boundaries for training the ANN. According to comparisons, there is a good agreement between experimental and neural network results


2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  

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