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Tool remaining useful life prediction method based on LSTM under variable working conditions

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Abstract

Tool remaining useful life prediction is important to guarantee processing quality and efficient continuous production. Tool wear is directly related to the working conditions, showing a complex correlation and timing correlation, which makes it difficult to predict the tool remaining useful life under variable conditions. In this paper, we seek to overcome this challenge. First, we establish the unified representation of the working condition, then extract the wear characteristics from the processing signal. The extracted wear features and corresponding working conditions are combined into an input matrix for predicting tool wear. Based on this, the complex spatio-temporal relationship under variable working conditions is captured. Finally, using the unique advantages of the long short-term memory (LSTM) model to solve complex correlation and memory accumulation effects, the tool remaining useful life prediction model under variable working conditions is established. An experiment illustrates the effectiveness of the proposed method.

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Funding

The work is financially supported by the National Defense Basic Scientific Research program of China through approval no. JSCG2016205B006 and the National Science and Technology Major Project of China through approval no. 2012ZX04011041.

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Authors and Affiliations

  1. Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Xi’an, Shaanxi Province, China

    Jing-Tao Zhou, Xu Zhao & Jing Gao

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  1. Jing-Tao Zhou
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  2. Xu Zhao
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  3. Jing Gao
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Correspondence to Jing-Tao Zhou.

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Zhou, JT., Zhao, X. & Gao, J. Tool remaining useful life prediction method based on LSTM under variable working conditions. Int J Adv Manuf Technol 104, 4715–4726 (2019). https://doi.org/10.1007/s00170-019-04349-y

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