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International Conference on Advanced Computer Theory and Engineering (ICACTE 2009)

Xie Yi
Xie Yi
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In this study cutting forces prediction was modeled using back propagation (BP) neural network algorithm. Experimental turning dataset is used in this study to train and evaluate the model. The Input dataset includes speed, feed rate, depth of cut, vibration levels along the three axes on tool holder (ax,ay,az). The Output dataset includes feed force, vertical force, and radial force. Marginally acceptable results were given by early experiments of this study and when data was examined, high non-linearity can be seen from the prepared graphic. In the previous work, a fine development of reliability of predicting the cutting forces can be observed by the help of results. To compare the estimated results of cutting force from this method with the cutting force signal can be measured directly by dynamometer; it is found that the difference between measured and estimated cutting forces is less than 0.2% in all case.

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