An AI-powered system for monitoring the condition of CNC tools, for early chatter recognition in machine prevents significant losses in the manufacturing industry caused by machine breakdowns. Machine condition monitoring is the practice of observing and tracking specific parameters of a machine in order to detect any major changes that may indicate an impending failure. Continuous machine monitoring enables proactive prevention of failure by detecting and addressing potential issues before they occur. In today's highly competitive environment, manufacturing industries prioritize the production of high-quality products while maximizing productivity. Increasing the Material Removal Rate (MRR) can improve the productivity of various manufacturing industries.

This book provides a comprehensive examination of the literature on the phenomena of chatter. Based on this analysis, it has been deduced that researchers have proposed several approaches for identifying and suppressing chatter. However, these strategies have not been widely used in the industry. The primary aim of this book is to offer a methodology for determining the ideal range of process parameters for achieving stable machining with a higher material removal rate. The method encompasses data acquisition, study of sensitive positions to choose appropriate sensor placements, signal pre-processing, feature extraction, feature selection, classification, and optimization techniques. Moreover, in this book both traditional as well as non-traditional (Artificial Intelligence) techniques have been presented. These techniques are being used in industries for achieving better products along with higher productivity.