DIGITAL TWIN-ASSISTED TOOL WEAR PREDICTION FOR ADAPTIVE INTELLIGENT MACHINING SYSTEMS

Abstract

Tool wear significantly influences machining quality, production efficiency, dimensional accuracy, and manufacturing cost in modern intelligent machining systems. Conventional tool condition monitoring techniques rely on periodic inspection and predefined machining intervals, often resulting in unexpected tool failures, poor surface finish, increased downtime, and inefficient resource utilization. Digital Twin technology enables real-time synchronization between physical machining operations and virtual manufacturing models, allowing continuous prediction of tool wear and adaptive process optimization. This paper proposes a Digital Twin-assisted tool wear prediction framework by integrating machine learning, Industrial Internet of Things (IIoT), predictive analytics, cloud computing, and intelligent manufacturing. The proposed framework continuously analyzes machining parameters, spindle vibration, cutting forces, acoustic emissions, temperature, and tool condition to predict wear progression while dynamically optimizing machining operations. Experimental evaluation demonstrates significant improvements in prediction accuracy, machining quality, tool life, production efficiency, and intelligent manufacturing decision-making compared with conventional tool monitoring approaches. Keywords: Digital Twin, Tool Wear Prediction, Intelligent Machining, Machine Learning, Industrial Internet of Things, Predictive Analytics, Smart Manufacturing, CNC Machining, Adaptive Manufacturing, Industry 4.0.