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Three in-process tool wear monitoring systems have been developed in this research. They are: (1) the multiple linear regression based in-process tool wear prediction (MLR-ITWP) system; (2) the artificial neural networks based in-process tool wear prediction (ANN-ITWP) system; and (3) the statistics assisted fuzzy-nets based in-process tool wear prediction (S-FN-ITWP) system. Before these above-mentioned systems were developed and evaluated, statistical approaches had been implemented to analyze and identify the most significant force signal for tool wearing monitoring system. This study demonstrates that the average peak cutting forces in the Y direction (the direction that is perpendicular to the table feed) is the most effective cutting force representation for tool wear monitoring. Following with this discovery, the cutting parameters (feed rate and depth of cut) along with the average peak cutting force in the Y direction became input signal for developing a MLR-ITWP system. A multiple linear regression model was obtained through 100 experimental data sets. Another nine data sets were used to test the system. The average tool wear prediction error of the MLR-ITWP system was ±0.039 mm through

In-process tool wear prediction system development in end milling operations