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Tool wear state recognition based on gradient boosting decision tree and hybrid classification RBM

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Abstract

Machined surface quality and dimensional accuracy are significantly affected by tool wear in machining process. Tool wear state (TWS) recognition is highly desirable to realize automated machining process. In order to improve the accuracy of TWS recognition, this research develops a TWS recognition scheme using an indirect measurement method which selects signal features that are strongly correlated with tool wear to recognize TWS. Firstly, three time domain features are proposed, including dynamic time warping feature and two entropy features. The time, frequency, and time-frequency domain features of the vibration and force signals are extracted to form a feature set. Secondly, gradient boosting decision tree (GBDT) is adopted to select the optimal feature subset. Lastly, contrastive divergence (CD) and RMSspectral are used to train hybrid classification RBM (H-ClassRBM). The trained H-ClassRBM is used for TWS recognition. The PHM challenge 2010 data set is used to validate the proposed scheme. Experimental results show that the proposed features have better monotonicity and correlation than the classical features. Compared with CD and Adadelta, CD and Adagrad, and CD and stochastic gradient descent with momentum, the H-ClassRBM trained by CD and RMSspectral improves recognition accuracy by 1%, 2%, and 2%, respectively. Compared with feedforward neural network, probabilistic neural network, Gaussian kernel support vector machine, and H-ClassRBM, the proposed TWS recognition scheme improves recognition accuracy by 37%, 51%, 9%, and 8%, respectively. Therefore, the proposed TWS recognition scheme is beneficial in improving the recognition accuracy of TWS, and provides an effective guide for decision-making in the machining process.

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Funding

This work is supported by the State Key Science & Technology Program of China (Grant No. 2019ZX04012-001); National Natural Science Foundation of China (51905209); Industrial Technology Research and Development Project of Jilin Province Development and Reform Commission, China (2019C040-2); Young and Middle-aged Scientific and Technological Innovation leaders and Team Projects in Jilin Province, China (20190101015JH); and Program for JLU Science and Technology Innovative Research Team (JLUSTIRT).

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Authors and Affiliations

  1. Key Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin University, Changchun, Jilin, China

    Guofa Li, Yanbo Wang, Jialong He, Haiji Yang & Jingfeng Wei

  2. School of Mechanical and Aerospace Engineering, Jilin University, Changchun, 130022, China

    Guofa Li, Yanbo Wang, Jialong He, Haiji Yang & Jingfeng Wei

  3. Office of Academic Affairs, Aviation University of Air Force, Changchun, 130000, China

    Qingbo Hao

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  1. Guofa Li
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  2. Yanbo Wang
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  3. Jialong He
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Correspondence to Jialong He.

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Li, G., Wang, Y., He, J. et al. Tool wear state recognition based on gradient boosting decision tree and hybrid classification RBM. Int J Adv Manuf Technol 110, 511–522 (2020). https://doi.org/10.1007/s00170-020-05890-x

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