Integrating Taguchi principle with genetic algorithm to minimize burr size in drilling of AISI 316L stainless steel using an artificial neural network model

2007 ◽  
Vol 221 (12) ◽  
pp. 1695-1704 ◽  
Author(s):  
S R Karnik ◽  
V Gaitonde ◽  
J Paulo Davim
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Stainless Steel ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
316L Stainless Steel ◽  
Aisi 316L ◽  
Aisi 316L Stainless Steel ◽  
Burr Size

Genetic algorithm-based burr size minimization in drilling of AISI 316L stainless steel

2008 ◽  
Vol 197 (1-3) ◽  
pp. 225-236 ◽  
Author(s):  
V.N. Gaitonde ◽  
S.R. Karnik ◽  
B.T. Achyutha ◽  
B. Siddeswarappa
Keyword(s):  
Genetic Algorithm ◽  
Stainless Steel ◽  
316L Stainless Steel ◽  
Aisi 316L ◽  
Aisi 316L Stainless Steel ◽  
Burr Size

scholarly journals Improving applicability of neuro-genetic algorithm to predict short-term water level: a case study

2013 ◽  
Vol 16 (1) ◽  
pp. 218-230 ◽  
Author(s):  
Gooyong Lee ◽  
Sangeun Lee ◽  
Heekyung Park
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Water Level ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
River Management ◽  
Water Levels ◽  
Short Term

This paper proposes a practical approach of a neuro-genetic algorithm to enhance its capability of predicting water levels of rivers. Its practicality has three attributes: (1) to easily develop a model with a neuro-genetic algorithm; (2) to verify the model at various predicting points with different conditions; and (3) to provide information for making urgent decisions on the operation of river infrastructure. The authors build an artificial neural network model coupled with the genetic algorithm (often called a hybrid neuro-genetic algorithm), and then apply the model to predict water levels at 15 points of four major rivers in Korea. This case study demonstrates that the approach can be highly compatible with the real river situations, such as hydrological disturbances and water infrastructure under emergencies. Therefore, proper adoption of this approach into a river management system certainly improves the adaptive capacity of the system.

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