Prediction of Tool Wear by Using RGB Techniques in Comparison with Experimental Analysis

Abstract

Cutting tool wear is the single biggest cause of lost productivity in the manufacturing industry. Monitoring and diagnosing the cutting tool wear condition prior to failure is crucial for improving machining quality and maximising production profits. Since tool wear has such a significant impact on a manufacturing company’s day-to-day operations, it is crucial to keep a close eye on the cutting tool as it is machined. To do this, an image was captured using a camera after each machining cycle, then processed in MATLAB with red-green-blue (RGB) techniques, and the results were compared with those obtained using an experimental method. This research aims to provide experimental evidence for the effectiveness of image capture and processing techniques for use in assessing tool wear in a versatile manufacturing cell environment.