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Machine tool operating vibration prediction based on multi‐sensor fusion and LSTM neural network
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Machine tool operating vibration prediction based on multi‐sensor fusion and LSTM neural network
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Machine tool operating vibration prediction based on multi‐sensor fusion and LSTM neural network
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Journal Article
Machine tool operating vibration prediction based on multi‐sensor fusion and LSTM neural network
2024
Overview
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. A machine tool operating vibration prediction method based on multi‐sensor fusion (Kalman) and long short‐term memory network is proposed
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