Tool Wear

Tool wear monitoring aims to detect and predict the degradation of cutting tools in manufacturing processes, improving product quality and reducing downtime. Current research heavily utilizes machine learning, particularly deep learning architectures like convolutional neural networks (CNNs) and U-Nets, often combined with sensor data (acoustic emissions, force signals, images) to achieve accurate wear segmentation and remaining useful life (RUL) prediction. These advancements enable more precise predictive maintenance, leading to optimized resource allocation and enhanced efficiency in various manufacturing applications, including milling, turning, and ultrasonic welding. Furthermore, research is exploring model interpretability and robustness, particularly in handling limited datasets and adapting to diverse industrial settings.