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Advances in Materials Processing Technologies: Mesic V by Juan José Aguilar Martin, José Antonio Yagüe Fabra

By Juan José Aguilar Martin, José Antonio Yagüe Fabra

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29 33 37 41 45 Fig. 2. Validation of the selected regression model and neuronal network. 46 Advances in Materials Processing Technologies Table 5. Predictive characteristics of the selected models. 30 Conclusions Cutting force (Fx, Fy, Fz) signals were the most significant, and were the primary means for estimating the arithmetic mean roughness (Ra). The models based on these signals provided the best fit and highest predictions, with the lowest mean relative prediction errors ̅ . In comparison, the machine vibration (ax, ay, az) signals, and the acoustic emission (EARMS) signal had little influence on the Ra roughness parameter, and failed to provide relevant data on their own; notwithstanding, these signals can slightly improve the performance of predictive models when combined with cutting force signals.

A) b) Fig. 3. a) Cutting forces evaluation zone (framed). b) Cutting force components in slotting.

Though the separate analysis of acceleration and acoustic emission 44 Advances in Materials Processing Technologies sensors did not offer good results, slightly better predictions were obtained for neuronal network configurations that were combined with cutting forces. Table 2 shows no tendency was found between the mean relative prediction error ( ̅ ) and the different network configurations of the varying number of layers and number of neurons. Table 2. Mean relative errors ̅ (%) of the artificial neuronal network models.

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