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Ingeniería, investigación y tecnología

versión impresa ISSN 1405-7743

Resumen

MOTA-VALTIERRA, G.C.; FRANCO-GASCA, L.A.; HERRERA-RUIZ, G  y  MACIAS-BOBADILLA, G. ANN Based Tool Condition Monitoring System for CNC Milling Machines. Ing. invest. y tecnol. [online]. 2011, vol.12, n.4, pp. 461-468. ISSN 1405-7743.

Most of the companies have as objective to manufacture high-quality products, then by optimizing costs, reducing and controlling the variations in its production processes it is possible. Within manufacturing industries a very important issue is the tool condition monitoring, since the tool state will determine the quality of products. Besides, a good monitoring system will protect the machinery from severe damages. For determining the state of the cutting tools in a milling machine, there is a great variety of models in the industrial market, however these systems are not available to all companies because of their high costs and the requirements of modifying the machining tool in order to attach the system sensors. This paper presents an intelligent classification system which determines the status of cutters in a Computer Numerical Control (CNC) milling machine. This tool state is mainly detected through the analysis of the cutting forces drawn from the spindle motors currents. This monitoring system does not need sensors so it is no necessary to modify the machine. The correct classification is made by advanced digital signal processing techniques. Just after acquiring a signal, a FIR digital filter is applied to the data to eliminate the undesired noisy components and to extract the embedded force components. A Wavelet Transformation is applied to the filtered signal in order to compress the data amount and to optimize the classifier structure. Then a multilayer perceptron-type 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.

Palabras llave : breakage; wear; Wavelet transform; artificial neural networks; monitoring system; FIR filter.

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