Abstract :
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.
Keywords :
cutting; cutting tools; deformation; discs (structures); electric current measurement; force measurement; mechanical engineering computing; neural nets; pattern classification; power measurement; signal processing; velocity measurement; voltage measurement; ANN; Afyon Kocatepe University; artificial neural networks; axial force measurement; broken disc; classification application; current measurement; cutting disc; cutting process; deformation class; deformation classification; feature vector; less damaged disc; marble cutting machine; noise measurement; peripheral speed measurement; power measurement; undamaged disc; very damaged disc; voltage measurement; Artificial neural networks; Conferences; Force; Hidden Markov models; Machine tools; Monitoring; Signal processing; Artificial Neural Network; Classification; Marble cutting;
