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Volume 42 Issue 1
Mar.  2022
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Article Navigation >  Diamond & Abrasives Engineering  > 2022  >  42(1): 104-111
ZHU Huanhuan, LI Houjia, ZHANG Mengmeng, TAN Shaodong, CHI Yulun. On-line identification and monitoring method for external grinding flutter based on BP neural network[J]. Diamond & Abrasives Engineering, 2022, 42(1): 104-111. doi: 10.13394/j.cnki.jgszz.2021.0097
Citation: ZHU Huanhuan, LI Houjia, ZHANG Mengmeng, TAN Shaodong, CHI Yulun. On-line identification and monitoring method for external grinding flutter based on BP neural network[J]. Diamond & Abrasives Engineering, 2022, 42(1): 104-111. doi: 10.13394/j.cnki.jgszz.2021.0097
shu

On-line identification and monitoring method for external grinding flutter based on BP neural network

doi: 10.13394/j.cnki.jgszz.2021.0097
  • 1.

    Higher Vocational Technical College, Shanghai University of Engineering Science, Shanghai 200437, China

  • 2.

    Department of Manufacturing Engineering, Shanghai Technician School, Shanghai 200437, China

  • 3.

    College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

More Information
  • Received Date: 2021-08-11
  • Accepted Date: 2021-10-18
  • Rev Recd Date: 2021-09-21
    Abstract
  • To improve the ability of the machine tool to identify chatter during the grinding process, a chatter recognition method is proposed based on the BP (back propagation) neural network model. By extracting the relevant feature values of the high-frequency acoustic emission signals and vibration signals in the processing process, multi-feature signal samples library about flutter are obtained. The multi-feature signal sample library is used to learn and train the BP neural network to establish recognition model. The model realizes on-line monitoring and accurate identification of whether chattering occurring during machine tool processing. The experimental results show that the flutter recognition based on the BP neural network model verifies that the measured test results are consistent with the actual flutter and network recognition results. Therefore, this method can effectively identify the flutter phenomenon in the processing process and play the role of online intelligent monitoring.

     

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