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This paper deals with simple neural network-based diagnostic system, applied to tool wear prediction in MDF milling. Ten tools were used for the test, and each one was consequently worn in the process of MDF milling. During the wearing process, the key process parameters were measured, such as cutting and thrust forces, temperature and power consumption. The neural network-based system was used for tool wear prediction of all the tools except the fi rst one, based on data collected during the previous attempts. The test has shown that the proposed system has good prediction accuracy and that it could be a useful tool in the optimization of the woodworking process.U radu se opisuje primjena neuronskih mreža za jednostavan dijagnostički sustav kojim se predviđa trošenje alata pri glodanju MDF (Medium Density Fiberboard) ploča. Za test je uporabljeno deset alata koji su radi istraživanja zatupljivani glodanjem MDF ploča. Tijekom procesa trošenja alata mjereni su temeljni parametri procesa glodanja kao što su sila rezanja i odrivna sila, temperatura i angažirana snaga. Sustav utemeljen na neuronskoj mreži primijenjen je za predviđanje stadija zatupljenosti svih alata osim prvoga, na osnovi podataka dobivenih mjerenjima na prethodnim alatima. Istraživanje je pokazalo da predloženi sustav ima dobru točnost predviđanja i mogao bi biti dobar alat za optimiziranje procesa obrade drva

Application of Neural Network in Simple Tool Wear Monitoring and Indentification System in MDF Milling