THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN GRINDING OPERATION USING SENSOR FUSION

Abstract

The application of multi-sensor systems for the monitoring of machining processes is becoming more
commonplace to improve productivity, automation and reliability. In order to enhance knowledge in this area of
applications, this study proposes a novel approach for the continuous on-line condition monitoring of grinding
operation using low cost infrared and visual imager alongside with more commonly used sensors i.e. AE sensor,
accelerometer and dynamometer. To achieve this aim a multi-sensor system is developed and installed for the
monitoring of grinding operation. The signals acquired and analyzed by the system include visual, thermal, force,
vibration and AE under different grinding conditions. Image processing techniques are used to establish that an
increase in sparks within grinding zone results in rise of grinding zone temperature, which in turn results in
increased surface roughness. Signal processing techniques are used to establish that dressing of wheel is most
influential factor for surface roughness of workpiece. Artificial intelligence is then used successfully on both
infrared and visual data to establish an automated continuous on-line monitoring system for grinding operation with
an accuracy of 95 percent.