Abstract
Development of artificial neural network (ANN) for prediction of aluminum workpieces' surface roughness in ultrasonic- vibration assisted turning (UAT) has been the subject of the present study. Tool wear as the main cause of surface roughness was also investigated. ANN was trained through experimental data obtained on the basis of full factorial design of experiments. Various influential machining parameters were taken into consideration. It was illustrated that a multilayer perceptron neural network could efficiently model the surface roughness as the response of the network, with an error less than ten percent. The performance of the trained network was verified by further experiments. The results of UAT were compared with the results of conventional turning experiments carried out with similar machining parameters except for the vibration amplitude whence considerable reduction was observed in the built-up edge and the surface roughness. Keywords—Aluminum, Artificial Neural Network (ANN), Built- up Edge, Surface Roughness, Tool Wear, Ultrasonic Vibration Assisted Turning (UAT).
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