الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
In the present investigation, the artificial neural networks approach was
adopted to predict the machining characteristics of epoxy matrix nanocomposites reinforced with multi-wall carbon nanotubes. The material removal rate, surface roughness and roundness error machining characteristics of the epoxy resin
reinforced with multi-wall carbon nanotubes was evaluated during turning. the effect of the depth-of-cut, cutting speed and feed rate on the aforementioned responses were evaluated. Different artificial neural network models based on multilayer perceptron networks were developed.
The results revealed the usefulness and effectiveness of the developed
artificial neural network models in predicting the machining characteristics with a very good accuracy. The cutting speed is the most significant factor that affects the material removal rate, Wile the feed rate is the most significant factor that affects
the surface roughness and roundness error .