Material Removal Rate Prediction of EDDG of PCBN Cutting Tool Inserts Based on Experimental Design and Artificial Neural Networks

2018 ◽  
pp. 481-489
Author(s):  
Rohit Shandley ◽  
Siddharth Choudhary ◽  
S. K. Jha
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Experimental Design ◽  
Material Removal Rate ◽  
Material Removal ◽  
Cutting Tool ◽  
Removal Rate ◽  
Rate Prediction ◽  
Artificial Neural

Soft Modeling of Wire Electrical Discharge Machining of WC-Co Composite

2011 ◽  
Vol 335-336 ◽  
pp. 535-540 ◽  
Author(s):  
Veluswamy Muthuraman ◽  
Raju Ramakrishnan
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Artificial Neural Networks ◽  
Material Removal Rate ◽  
Electrical Discharge ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Wire Electrical Discharge Machining ◽  
Ann Model

The prediction of optimal machining conditions for required surface roughness and material removal rate (MRR) plays a very significant role in process planning of wire electrical discharge machining (WEDM). Artificial neural networks (ANN) are widely applied to predict the performance characteristics of complex machining process like WEDM very accurately. This present work deals with the features of cutting operation by WEDM of tungsten carbide- cobalt composite(WC – Co) and an artificial neural networks(ANN) model in terms of machining parameters, developed to predict surface roughness(Ra) and material removal rate (MRR).The experiment was planned as per Taguchi’s L 27 orthogonal array. The predictive capacity of the models was validated. The test results indicate that the proposed models could adequately describe the performance indicators with the limits of the factors that are being investigated. Finally the accuracy of the developed ANN model was compared to the experimental values. It was observed that the proposed ANN model is good.

Corrosion rate prediction for metals in biodiesel using artificial neural networks

2019 ◽  
Vol 140 ◽  
pp. 592-601 ◽  
Author(s):  
C.I. Rocabruno-Valdés ◽  
J.G. González-Rodriguez ◽  
Y. Díaz-Blanco ◽  
A.U. Juantorena ◽  
J.A. Muñoz-Ledo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Corrosion Rate ◽  
Rate Prediction ◽  
Artificial Neural
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