Research progress on intelligent monitoring of tool condition based on deep learning

Intelligent monitoring of tool condition is the key to ensuring workshop manufacturing efficiency, product machining quality, and accuracy, and is also an indispensable part of intelligent processing. In the face of complex and massive, multi-source heterogeneous, and low-value density machining process data, the monitoring method based on traditional machine learning is challenging to meet the development needs of intelligent manufacturing. In contrast, with its powerful data processing and automatic feature extraction capabilities, deep learning shows broad application prospects in tool condition intelligent monitoring. Given this, this paper first systematically introduces the components of tool condition intelligent monitoring framework based on deep learning. Subsequently, the basic principles, modeling process, and application status of the four most widely used deep learning models (deep belief network, stacked auto-encoder network, convolutional neural network, and recurrent neural network) in the field of tool condition monitoring are detailed, and the advantages and disadvantages of different models are comparably discussed. Finally, the challenges and prospects of the current tool condition intelligent monitoring based on deep learning are summarized.