Prediction of Surface Roughness in Turning of Ud-gfrp Using Artifical Neural Network
نویسنده
چکیده
The present investigation deals with the study and development of a surface roughness prediction model for the machining of unidirectional glass fiber reinforced plastics (UDGFRP) composite using Artificial Neural Network (ANN). The feed forward back propagation is used. Taguchi method (Orthogonal L16 array) is employed to carry out the experimental work. The process parameters selected for study are cutting speed, feed rate, depth of cut and cutting environment (dry and wet). The predicted values from surface roughness model are compared with the experimental values. It is clear from the results that predicted roughness matches with the experimental data and the correlation coefficient is found to be more than 0.9.
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