In the present work, a prediction model of the surface roughness in the dry turning of UNS A97075 aeronautical aluminum alloy was developed using artificial neural network (ANN). Specifically, the cutting speed and feed rate influence on the maximum height of roughness profile has been analyzed, due to its influence in the fatigue behavior of machined parts. Furthermore, the effect of the network architecture, such as the optimal number of neurons in the hidden layer, and the selection of experimental results applied in the ANN’s training and validation was studied to determine the best fit, minimizing the root mean square error. The use of ANNs has been shown to be a useful tool for such regression task, obtaining a high level of fit, even higher than the existing models in the literature in this regard.

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