Research on tap breakage monitoring method for tapping process based on SSAELSTM fusion network

Aiming at the problems of high tap breakage rate during tapping of small-diameter internal threads and the difficulty of feature extraction and fusion in characterising tap breakage, this paper proposes a tap breakage monitoring method based on stack sparse autoencoder long-short-term memory (SSAELSTM) fusion network. Specifically, based on change rule of torque in the tapping process and the analysis of the processing mechanism, the normal label and the breakage label are defined, and 36 groups of feature in the multi-feature domain of the torque signal are extracted, constituting a labelled set of feature. Additionally, a stacked sparse autoencoder network (SSAE) is employed to fuse the feature that serve as inputs to the long short-term memory network (LSTM) for recognition, and the classification errors of LSTM are back-propagated to SSAE to optimize the weights of the LSTM and SSAE networks. Finally, 31 SSAELSTM fusion network models with different structures are constructed, and the final tap breakage monitoring model is determined by comparing the performance indexes. Compared with the features before fusion, the constructed model fusion effectively reduces the amount of data and improves the correlation with the labels. Compared with other network models, it was found that SSAELSTM fusion network is superior to other models in both accuracy and robustness, providing breakage warning for the tapping process, and laying the foundation for subsequent decision-making to avoid tap breakage.