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Tool wear prediction using a feed-forward backpropagation (FFBP) ANN

Tool wear prediction using a feed-forward backpropagation (FFBP) ANN

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Conference Paper
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Manufacturers have faced an increasing need for the development of predictive models that help predict mechanical failures and remaining useful life of a manufacturing system or its system components. Model-based or physics-based prognostics develops mathematical models based on physical laws or probability distributions, while an in-depth physical...

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... is a learning algorithm for training ANNs in conjunction with an optimization method such as gradient descent. Figure 1 illustrates the architecture of the FFBP ANN with a single hidden layer. In this research, the ANN has three layers, including input layer i, hidden layer j, and output layer k. ...
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... different number of neurons were tested on the training dataset. Tables 4-8 list the MSE, R-squared, and training time for the ANNs with 2, 4, 8, 16, and 32 neurons. With respect to the performance of the ANN, the training time increases as the number of neurons increases. However, the increased in training time are not significant as shown in Fig. 10. In addition, while the prediction accuracy increases as the number of neurons increases, the performance is not significantly improved by adding more than eight neurons in the hidden layer as shown in Figs. 11 and 12. Tables 9 and 10 list the MSE, R-squared, and training time for SVR and RFs. While the training time for RFs is longer ...

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