FREQUENCY AND TIME-DOMAIN ANALYSES OF SENSOR SIGNALS IN DRILLING .2. INVESTIGATION ON SOME PROBLEMS ASSOCIATED WITH SENSOR INTEGRATION

Sensor integration has received considerable attention recently far monitoring machining processes. This is because it is similar to the action of an experienced machinist, who uses his different sensory devices such as hearing, sight, etc. to monitor the cutting operation. Different neural network paradigms have been attempted by researchers for this purpose. In this investigation, a multisensor approach to drill wear monitoring was studied. Four sensors, namely, thrust, torque, and strains on the machine table in two orthogonal directions perpendicular to the drill axis, were used. As shown in Part I [A. Noori-Khajavi and R. Komanduri, Int. J. Mach. Tools Manufact. 35, 000-000 (1995)] three sensor signals, namely, thrust, torque, and strain on the machine table in the X-direction, showed good correlation in the frequency domain with drill wear. In addition, the signal-to-noise ratio analysis at different states of drill wear in the frequency domain showed that as the drill wear increased, the noise also increased. In this paper, it will be shown that when sensor signals are noisy and are integrated using a neural network, such a system could actually result in the deterioration of the correct estimation of drill wear. Consequently, there appears to be no need for the integration of the sensor signals under the conditions used.