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monitoring is one of \nthe key issues in highly precision manufacturing. Monitoring \nof machining operation not only reduces the need of expert \noperators but also reduces the chances of unexpected tool \nbreakage which may damage the work piece. In the present \nstudy, the tool wear of the micro drill and thrust force have \nbeen studied during the peck drilling operation of AISI P20 \ntool steel workpiece. Variations of tool wear with drilled \nhole number at different cutting conditions were \ninvestigated. Similarly, the variations of thrust force \nduring different steps of peck drilling were investigated \nwith the increasing number of holes at different feed and \ncutting speed values. Artificial neural network (ANN) model \nwas developed to fuse thrust force, cutting speed, spindle \nspeed and feed parameters to predict the drilled hole number. \nIt has been shown that the error of hole number prediction \nusing a neural network model is less than that using a \nregression model. 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