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Research on online intelligent monitoring system of band saw blade wear status based on multi-feature fusion of acoustic emission signals

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Abstract

An online intelligent band saw blade wear status monitoring system based on multi-feature fusion of acoustic emission signals is proposed to address the problem about sawing efficiency and quality degradation because of band saw blade wear. Firstly, the acoustic emission raw signal is obtained through the whole life cycle experiment. A variety of features highly correlated to the wear state of the band saw blade are extracted through the study on the wear mechanism of the band saw blade. Secondly, a cost-sensitive support vector machine based multi-feature fusion model of acoustic emission signals is established to implement real-time determination of the wear status of band saw blades. A database for online monitoring of the wear status of band saw blades was established considering the diversity and complexity of sawing parameters, processing materials, and saw machine models in different sawing scenarios. An online intelligent monitoring system based on LabVIEW and MATLAB are established for band saw blade wear status. Validation experiment results show that the online monitoring can achieve an accuracy of 97.71%.

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Funding

This research was financially supported by the Special Fund for the Construction of Hunan Innovative Province (Grant No. 2020GK2003), the municipal joint Fund for Natural Science of Hunan Provincial (Grant No. 2021JJ50116), the National Natural Science Foundation of China (grant No. U1809221), and Hunan University of Science and Technology and the Bichamp Cutting Technology (Hunan) Co., Ltd. School-enterprise cooperation project.

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Authors and Affiliations

  1. Hunan Provincial Key Laboratory of High Efficiency and Precision Machining of Difficult to Machine Material, Hunan University of Science and Technology, Xiangtan, 411201, Hunan, China

    Rongjin Zhuo, Zhaohui Deng, Bing Chen, Tao Liu, Jimin Ge & Shenghao Bi

  2. School of Mechanical Engineering, Hunan University of Science and Technology, Xiangtan, 411201, Hunan, China

    Rongjin Zhuo, Zhaohui Deng, Bing Chen, Tao Liu, Jimin Ge & Shenghao Bi

  3. Bichamp Cutting Technology (Hunan) Co., Ltd., Changsha, 410200, Hunan, China

    Guoyue Liu

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  1. Rongjin Zhuo
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  2. Zhaohui Deng
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  4. Tao Liu
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Contributions

Rongjin Zhuo: Conceptualization investigation, writing (original draft), writing (review and editing). Zhaohui Deng: Writing (review and editing), funding acquisition. Bing Chen: Writing (review and editing). Tao Liu: Writing (review and editing). Jinmin: Review and editing. Guoyue Liu: Funding acquisition. Shenghao Bi: Review and editing.

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Correspondence to Zhaohui Deng.

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Zhuo, R., Deng, Z., Chen, B. et al. Research on online intelligent monitoring system of band saw blade wear status based on multi-feature fusion of acoustic emission signals. Int J Adv Manuf Technol 121, 4533–4548 (2022). https://doi.org/10.1007/s00170-022-09515-3

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