Surface grinding wheels are employed to manufacture components with high precision and finishing. They are frequently used to improve the finish on flat, curved and angled surfaces. The process is capable of grinding metals, ceramics or composite workpiece materials. Condition monitoring of surface grinding wheels is important for maximum performance and extended life. This research aims to forecast the life of a grinding wheel using machine learning models trained on images captured after each grinding pass. The experimental system comprises a surface grinding machine, a DSLR camera and appropriate lighting to record images of the grinding wheel. The wheel is split into eight parts, and the pictures are taken at the end of each machining pass until the grinding wheel is loaded. Surface roughness generated on the workpiece at each machine pass is maintained throughout the grinding cycle. The recorded images are further segmented along the cutting face of the wheel. Relevant statistical image features, such as entropy, skewness, standard deviation, kurtosis and the number of embedded particles, are extracted using image clustering methods. They are correlated and labelled correctly using surface roughness measurements on the workpiece. The features are labelled as the initial, intermediate, and final stages. The conditions of the grinding wheel are trained, and models were tested using machine learning techniques such as classification and regression tree and support vector machines. The results show a strong correlation between the extracted image features and surface roughness during grinding. The support machine with a cubic kernel achieved the highest predictive accuracy of 91.16%. The model was also tested on data from another experiment conducted on the same wheel, achieving 83% classification accuracy.
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