| Summary: | Recently, cutting tool and product quality management in intelligent machining has been implemented by automated tool and quality monitoring and control systems. These systems utilize born features recognized in indirect signals, which reflect, on-line, the tool and quality conditions. In this research work, study was carried out to analyze the dynamic cutting signals of the end-milling process, in order to establish a force based model extracted from these signals, to monitor the end milling tool flank wear and workpiece surface roughness for intelligent machining. Experimental tests in end milling operations are carried out as a case study to verify the results of the proposed force model. The results showed that the proposed force model is an applicable method to predict the tool wear and surface roughness in end milling.
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