Machining Feature

Machining feature research focuses on optimizing manufacturing processes by analyzing and predicting various aspects of machining operations. Current research emphasizes the use of machine learning, particularly deep learning models like convolutional neural networks (CNNs) and other algorithms such as KNN and random forests, to analyze sensor data (e.g., acoustic emissions, force signals) and predict outcomes like tool wear, surface finish, and machining errors. This work aims to improve efficiency, reduce waste, and enhance the precision and predictability of manufacturing processes, impacting both industrial automation and materials science through improved process control and predictive maintenance.