Sensorless intelligent classifier of tool condition in a CNC milling machine using a SOM supervised neural network Online publication date: Tue, 27-Sep-2011
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, 27-Sep-2011
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Artificial Intelligence and Soft Computing (IJAISC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com