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Paper Information

Journal:   JOURNAL OF MODELING IN ENGINEERING   WINTER 2011 , Volume 8 , Number 23; Page(s) 25 To 36.
 
Paper: 

OPTIMISING THE ABRASIVE WATER JET CUTTING OF GLASS USING ARTIFICIAL NEURAL NETWORK AND GENETIC ALGORITHM

 
 
Author(s):  AMIRABADI H.*, ASHORI J., JAFARIAN F.
 
* FACULTY OF ENGINEERING, UNIVERSITY OF BIRJAND, BIRJAND, IRAN
 
Abstract: 

This paper proposes a hybrid approach based on the Artificial Neural network and Genetic algorithm to optimize surface roughness at the abrasive water jet (AWJ) cutting of glass material. At first, Artificial Neural Network (ANN) was developed in order to model and predict surface roughness by considering the controllable cutting parameters such as water pressure, abrasive flow rate, jet traverse rate and stand of distance. Then the results of the neural network were compared with corresponding experimental tests. According to the obtained results, it was shown that the ANN model is able to present a predictive model of the process in order to estimate the surface roughness successfully. After that, ANN model was combined by genetic algorithm to obtain suitable machining parameters yield to minimal surface roughness. Finally, obtained results showed that, utilized hybrid technique in this paper was employed properly for optimizing AWJ cutting process.

 
Keyword(s): ABRASIVE WATER JET CUTTING (AWJ), OPTIMIZATION, GENETIC ALGORITHM (GA), ARTIFICIAL NEURAL NETWORK (ANN), GLASS
 
References: 
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  Persian Abstract Yearly Visit 69
 
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