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Optimization of cutting conditions and prediction of surface roughness in AZ61 end milling

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Document pages: 8 pages

Abstract: In this paper, artificial neural network (ANN) and regression analysis are used to predict surface roughness. Five neural network models are established, and the model with 6 neurons in the hidden layer is most consistent with the experimental results. Regression analysis is also used to establish a mathematical model to express the surface roughness as a function of process parameters. Through 13 confirmatory experimental tests, it is found that the determination coefficients of the best neural network model and regression analysis are 94.93 and 93.63 respectively. Optical microscopy was performed on two machined surfaces with two different feed rate values while maintaining the same spindle speed and cutting depth. formermining the surface topology and surface roughness profile for the two surfaces revealed that higher feed rate results in relatively thick roughness markings that are distantly spaced, whereas low values of feed rate result in thin surface roughness markings that are closely spaced giving better surface finish.

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