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Tool wear recognition and signal labeling with small cross-labeled samples in impeller machining

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Abstract

Data-driven deep learning method is the main way to study the condition monitoring of mechanical equipment, in which sufficient labeled signals to train the model parameters is a typical problem. The existing methods to obtain the labeled signals mainly focus on manual marking. For the non-batch impeller processing with variable working conditions, manually marking signals is not the wisest move. To solve this problem, this manuscript puts forward a deep conditional random field neural network (CRFNN) method. This framework fully utilizes the sensitivity of the conditional probability model to adjacent data marker information, and small cross-labeled samples are used to predict the labels of unknown signals. At the same time, the variational autoencoder is used to convert the one-dimensional time series signal into a three-dimensional image, which solves the problem that the empty tool signals have a great impact on the tool wear condition monitoring in the process of impeller blade machining. Experimental results on a CNC machining center demonstrate the effectiveness and feasibility of the proposed method and outperform the existing works under industrial small labeled samples.

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Funding

This work was supported by the Dalian Science and Technology Innovation Funds (2021JJ12GX011), Dalian Science and Technology Innovation Funds (2020JJ25CY009), and National Natural Science Foundation of China under Grant U1808214.

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Authors and Affiliations

  1. School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, China

    Jiayu Ou, Hongkun Li, Zhaodong Wang, Chao Yang & Defeng Peng

Authors
  1. Jiayu Ou
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  2. Hongkun Li
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  3. Zhaodong Wang
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  4. Chao Yang
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  5. Defeng Peng
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Contributions

Jiayu Ou proposed the recognition method and wrote the paper. Hongkun Li guided the writing of the paper and acquired funding support. Zhaodong Wang collected the literature information and sorted out the experimental results. Chao Yang and Defeng Peng conducted experiments and obtained data.

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Correspondence to Hongkun Li.

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Ou, J., Li, H., Wang, Z. et al. Tool wear recognition and signal labeling with small cross-labeled samples in impeller machining. Int J Adv Manuf Technol 123, 3845–3856 (2022). https://doi.org/10.1007/s00170-022-10514-7

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