Neural network model for paper forming process

2002 ◽  
Author(s):  
J. Scharcanski ◽  
C.T.J. Dodson
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Forming Process

Neural network model for paper-forming process

1997 ◽  
Vol 33 (3) ◽  
pp. 826-839 ◽  
Author(s):  
J. Scharcanski ◽  
C.T.J. Dodson
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Forming Process

scholarly journals Development of a neural network model to predict distortion during the metal forming process by line heating

2013 ◽  
Vol 6 (12) ◽  
pp. 41 ◽  
Author(s):  
César Pinzón ◽  
Carlos Plazaola ◽  
Ilka Banfield ◽  
Amaly Fong ◽  
Adán Vega
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Initial Conditions ◽  
Training Data ◽  
Forming Process ◽  
Line Heating ◽  
Large Structures ◽  
Wide Range ◽  
Metal Forming Process

In order to achieve automation of the plate forming process by line heating, it is necessary to know in advance the deformation to be obtained under specific heating conditions. Currently, different methods exist to predict deformation, but these are limited to specific applications and most of them depend on the computational capacity so that only simple structures can be analyzed. In this paper, a neural network model that can accurately predict distortions produced during the plate forming process by line heating, for a wide range of initial conditions including large structures, is presented. Results were compared with data existing in the literature showing excellent performance. Excellent results were obtained for those cases out of the range of the training data.

Neural Network Model to Calculate the Creep Data Using Size

2017 ◽  
Vol 7 (8) ◽  
pp. 257
Author(s):  
Seetharam .K ◽  
Sharana Basava Gowda ◽  
. Varadaraj
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Software Engineering ◽  
Network Model ◽  
Neural Network Model ◽  
Software Metrics ◽  
Creep Data ◽  
Engineering Software ◽  
Function Points ◽  
Different Levels

In Software engineering software metrics play wide and deeper scope. Many projects fail because of risks in software engineering development[1]t. Among various risk factors creeping is also one factor. The paper discusses approximate volume of creeping requirements that occur after the completion of the nominal requirements phase. This is using software size measured in function points at four different levels. The major risk factors are depending both directly and indirectly associated with software size of development. Hence It is possible to predict risk due to creeping cause using size.

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