Abstract
Tool wear and unexpected tool failure are major issues in modern machining industries because they directly affect product quality, machining cost, and machine downtime. Traditional tool inspection methods depend mostly on manual checking or scheduled tool replacement which is not always efficient and can lead to unnecessary cost. Tool Condition Monitoring (TCM) has emerged as an important solution for predicting tool wear and detecting tool failure during machining operations. With the advancement of sensors, data acquisition systems and artificial intelligence, machine learning based monitoring methods are widely used. Sensor fusion further improves monitoring accuracy by combining signals from multiple sensors such as vibration, acoustic emission, cutting force and temperature. This paper reviews the concept of Tool Condition Monitoring using Machine Learning and Sensor Fusion techniques. It discusses the working principle, different sensors used for monitoring, data processing techniques and machine learning algorithms applied in TCM systems. The paper also highlights benefits, challenges and future research directions in this field. The study shows that integrating multiple sensors with machine learning models can significantly improve tool wear prediction accuracy and machining process reliability. However, challenges such as data quality, sensor noise, and model generalization still exist and require further research.
Keywords: Tool Condition Monitoring, Machine Learning, Sensor Fusion, Tool Wear Prediction, Smart Manufacturing, CNC Machining
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