In-process tool condition forecasting based on a deep learning method

Sun, Huibin, Zhang, Jiduo, Mo, Rong and Zhang, Xianzhi (2020) In-process tool condition forecasting based on a deep learning method. Robotics and Computer-Integrated Manufacturing, 64, p. 101924. ISSN (print) 0736-5845

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Abstract

It is widely acknowledged that machining precision and surface integrity are greatly affected by cutting tool conditions. In order to enable early cutting tool replacement and proactive actions, tool wear conditions should be estimated in advance and updated in real-time. In this work, an approach to in-process tool condition forecasting is proposed based on a deep learning method. A long short-term memory network is designed to forecast multiple flank wear values based on historical data. A residual convolutional neural network is built to enable in-process tool condition monitoring, using raw signals acquired during the machining process. The integration of them enables in-process tool condition forecasting. Median-based correction and mean-based correction are adopted to improve the accuracy. IEEE PHM 2010 challenge data has been used to illustrate and validate this approach. Experimental study and quantitative comparisons showed that future flank wear values could be precisely forecasted during the machining process. The proposed approach contributes to prompt and reliable cutting tool condition forecasting, which will support the decision-making about cutting tool replacement in data-driven smart manufacturing.

Item Type: Article
Additional Information: This work was supported by the National Natural Science Foundation of China [grant number: 5187545], the National Science and Technology Major Project [grant number: 2017-VII-0010-0104] and the Natural Science Basic Research Plan in Shaanxi Province of China [grant number: 2018ZDXM-GY-068].
Uncontrolled Keywords: tool condition forecasting; deep learning; long short-term memory; data correction
Research Area: Computer science and informatics
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing
Depositing User: Philip Keates
Date Deposited: 18 Feb 2020 11:13
Last Modified: 20 Feb 2020 08:29
DOI: https://doi.org/10.1016/j.rcim.2019.101924
URI: http://eprints.kingston.ac.uk/id/eprint/45014

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