Volume 13, Issue 8 (11-2013)
Modares Mechanical Engineering 2013, 13(8): 123-134 |
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khalili K, Foorginejad A, Ashory J. Modeling of abrasive water-jet cutting of glass using artificial neural network and optimization of surface roughness using firefly algorithm. Modares Mechanical Engineering 2013; 13 (8) :123-134
URL: http://mme.modares.ac.ir/article-15-4422-en.html
URL: http://mme.modares.ac.ir/article-15-4422-en.html
1- University of Birjand
Abstract: (6864 Views)
Abstract- In this paper, it is shown how to use the recently developed Firefly Algorithm to optimize abrasive water-jet cutting as a nonlinear multi-parameter process. Back propagation neural network were developed to predict surface roughness in abrasive water-jet cutting (AWJ) process. In the development of predictive models, machining parameters of traverse speed, water-jet pressure, standoff distance and abrasive flow rate were considered as model variables. Firefly Algorithm by using back propagation neural network optimizes glass surface roughness in abrasive water-jet cutting and proposes appropriate parameters for minimum surface roughness. Testing results demonstrate that the model is suitable for predicting the response parameters. However this algorithm has not be tested for practical problems, the results showed this algorithm applicable for processes with complex nature.
Article Type: Research Article |
Subject:
Industrial Manufacturing|Production Methods|Manufacturing Methods
Received: 2012/12/24 | Accepted: 2013/03/9 | Published: 2013/08/23
Received: 2012/12/24 | Accepted: 2013/03/9 | Published: 2013/08/23
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