Deformation classification of cutting discs using artificial neural networks

In this study, a classification application is realized to determine the deformation status of the cutting disc. 673 cutting experiments data obtained from a marble cutting machine suited in a laboratuvary of the Afyon Kocatepe University are evaluated for this purpose. During the cutting process, 8 different signals (Axial forces (Fx, Fy, Fz), Noise, Peripheral speed of the disc, Current, Voltage and Power) are measured and collected. The mean values of the each experiments are used as a 8 length feature vector. To determine the deformation class of the disc (undamaged, less damaged, very damaged and broken) these feature vectors are used. On the other hand Artificial Neural Networks (ANNs) are employed as classifiers. It is obtained that proposed method is able to classify the deformation status of the disc with 95,86 % accuracy.