Machine tool operating vibration prediction based on multi‐sensor fusion and LSTM neural network

Abstract

This study proposes a machine tool vibration prediction method based on multi‐sensor fusion and a long short‐term memory (LSTM) network. Machine tool vibration significantly impacts machining quality, surface roughness, dimensional accuracy, and tool wear. By combining deep learning with industrial applications, this method achieves high‐precision vibration prediction through multi‐sensor data fusion. Data is input into the LSTM model to predict the next moment's vibration. Experimental results demonstrate strong prediction capability for periodic vibrations and machining‐specific vibration errors, effectively enhancing machining accuracy.