Title: Sensorless intelligent classifier of tool condition in a CNC milling machine using a SOM supervised neural network
Authors: Georgina Del Carmen Mota-Valtierra; Luis Alfonso Franco-Gasca; Gilberto Herrera-Ruiz
Addresses: Facultad de Ingenieria, Laboratorio de Mecatronica, Universidad Autonoma de Queretaro, Cerro de las campanas s/n, Queretaro, Qro. CP 76010, Mexico. ' CIATEQ, A.C., LabCASD, Av. del Retablo 150, Col. Fovisste, Queretaro, Qro. CP 76150, Mexico. ' Facultad de Ingenieria, Laboratorio de Mecatronica, Universidad Autonoma de Queretaro, Cerro de las campanas s/n, Queretaro, Qro. CP 76010, Mexico
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.
Keywords: tool breakage; wear monitoring; sensorless classifiers; intelligent classification; tool condition monitoring; CNC milling; SOM neural networks; supervised neural networks; tool wear; tool monitoring; wavelet transforms.
International Journal of Artificial Intelligence and Soft Computing, 2011 Vol.2 No.4, pp.263 - 271
Available online: 27 Sep 2011 *Full-text access for editors Access for subscribers Purchase this article Comment on this article