Conditioning and monitoring of grinding wheels: A state-of-the-art review

Abstract

Grinding wheel condition monitoring is an important step towards the prediction of grinding wheel faulty conditions. It is beneficial to define techniques to minimize the wear of the grinding wheels and finally enhance the life of the grinding wheels. Grinding wheel condition monitoring is done by two techniques such as (i) direct and (ii) indirect. Direct monitoring employs optical sensors and computer vision techniques, and indirect monitoring is done by signal analysis such as acoustic emission (AE), vibration, cutting force, etc. Methods implemented for grinding wheel monitoring in the published research papers are reviewed. The review is compiled in five sections: (a) process parameters measurement, (b) data acquisition systems, (c) signal analysis techniques, (d) feature extraction, and (e) classification methods. In today’s era of Industry 4.0, a large amount of manufacturing data is generated in the industry. So, conventional machine learning techniques are insufficient to analyze real-time conditioning monitoring of the grinding wheels. However, deep learning techniques such as artificial neural network (ANN), convolutional neural network (CNN) have shown prediction accuracy above 99%.