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Tool wear prediction based on convolutional bidirectional LSTM model with improved particle swarm optimization

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Abstract

Accurate tool wear prediction is crucial for preventive maintenance on time. Most of the existing data-driven prediction methods still need complex feature engineering, which reduces the prediction accuracy and efficiency. To address this problem, a tool wear prediction model based on Improved Particle Swarm Optimization (IPSO) Convolutional Neural Network (CNN) and Bidirectional long short-term memory (BiLSTM) network is proposed. Firstly, the cutting force, vibration, and acoustic emission signals are taken as the input features of the model. CNN is used for high-dimensional feature extraction in the raw signal. Then, the BiLSTM with long-term memory and time-series processing ability is used to model time-series data. Besides, IPSO is employed to enhance the prediction accuracy of the hybrid model. Finally, the fully connected layer obtains the tool wear prediction results. IEEE PHM 2010 challenge data was used to illustrate and validate this model. The experimental results show that the hybrid model has an average prediction error of 7.06% on this data set, with an average absolute error (MAE) and an average root mean square error (RMSE) of 1.49 and 1.81, respectively. The prediction performance of the proposed hybrid model outperforms other related deep learning models.

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Data availability

The acquisition website of these data sets is provided in this study.

Code availability

It declares that codes are not available for this research.

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Funding

This study was supported by Guangxi Key Laboratory of Manufacturing System and Advanced Manufacturing Technology (Grant No.19–050-44-S006).

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Authors and Affiliations

  1. Department of Mechanical Engineering, Guangxi University, Nanning, 530004, China

    Xianwang Li, Xuejing Qin, Jinxin Wu, Jinfei Yang & Zhongxiang Huang

  2. Guangxi Key Laboratory of Manufacturing Systems and Advance Manufacturing Technology, Guangxi University, Nanning, 530004, China

    Xianwang Li

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Contributions

XL: supervision, project administration, funding acquisition, writing—review and editing. XQ: methodology, experiment, software, writing—original draft. JW: validation, investigation. JY: data curation. ZH: visualization.

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Correspondence to Xianwang Li.

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Li, X., Qin, X., Wu, J. et al. Tool wear prediction based on convolutional bidirectional LSTM model with improved particle swarm optimization. Int J Adv Manuf Technol 123, 4025–4039 (2022). https://doi.org/10.1007/s00170-022-10455-1

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