Analytical modeling of tool health monitoring system using multiple sensor data fusion approach in hard machining

Abstract

In recent years, a lot of work has been carried out on advance materials, especially hard metals and alloys, which is a vital requirement in many manufacturing industries. Nowadays steel with hardness beyond 30 HRC is employed in aircraft, hydraulics, car part manufacturing areas, etc. In the machining process, as the hardness of work material increases, tool health prone to deteriorate. Therefore, for healthier machinability requirements in machining of hard metals, the online tool health monitoring systems (THMS) are being developed using different feedback techniques. This study mainly concentrates on the advancement of online THMS using multiple sensors. Force, tool vibration, and surface roughness signals were recorded while machining EN24 hardened steel using coated carbide insert on CNC lathe. A novel analytical model of sensor data fusion has been presented for better understanding of sensors and their interaction. A comparative assessment of sensor fusion approach has been investigated. Experimental findings have successfully shown that the results obtained using a fusion function (U), validates better confirmability over single sensor-based approach.