A review on advanced monitoring and identifying the status of grinding machine using machine learning algorithms

Abstract

Recently, the development of the grinding process is improved by including automatic sensors, actuators, control systems, artificial intelligence, and industrial internet of things in the grinding machine. Already existing contact type techniques are replaced with the automatic sensor system to incorporate artificial intelligence in the normal grinding process. Therefore, optical and laser technology is emerging as a smart device to observe the surface topography of the grinding wheel, surface finish of the ground surface, and automatic dressing process. Moreover, artificial intelligence consists of machine learning which teaches the grinding machine based on the existing data available to improve the quality and the production rate of the grinding process. This can be achieved by controlling the process parameter, monitoring the machine's health, and attaining optimum conditions. The present review paper addresses the existing problems associated with the contact type and non-contact type measurement. The study also emphasizes the importance of machine learning algorithms to predict the failure of the grinding wheel and the surface roughness of workpiece material in the grinding process.