Sensorless intelligent classifier of tool condition in a CNC milling machine using a SOM supervised neural network Online publication date: Tue, 31-Mar-2015
by Georgina Del Carmen Mota-Valtierra; Luis Alfonso Franco-Gasca; Gilberto Herrera-Ruiz
International Journal of Artificial Intelligence and Soft Computing (IJAISC), Vol. 2, No. 4, 2011
Abstract: Industry has monitoring systems to determine the tool condition and to ensure quality. This paper presents an intelligent classification system which determines the status of cutters in a CNC milling machine. The tool states are detected through the analysis of the cutting forces drawn from the spindle motors currents. A wavelet transformation was used in order to compress the data and to optimise the classifier structure. Then a supervised SOM neural network is responsible for carrying out the classification of the signal. Achieving a reliability of 95%, the system is capable of detecting breakage and a worn cutter.
Online publication date: Tue, 31-Mar-2015
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