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Research on Intelligent Monitoring of Boring Bar Vibration State Based on Shuffle-BiLSTM

Due to its low stiffness, the boring bar used in deep-hole-boring is prone to violent vibration during the cutting process. It is often inaccurate and inefficient to judge the vibration state of the boring bar through artificial experience. To detect the change of the vibration state of the boring b...

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Autores principales: Liu, Qiang, Li, Dingkun, Ma, Jing, Bai, Zhengyan, Liu, Jiaqi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10347281/
https://www.ncbi.nlm.nih.gov/pubmed/37447972
http://dx.doi.org/10.3390/s23136123
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author Liu, Qiang
Li, Dingkun
Ma, Jing
Bai, Zhengyan
Liu, Jiaqi
author_facet Liu, Qiang
Li, Dingkun
Ma, Jing
Bai, Zhengyan
Liu, Jiaqi
author_sort Liu, Qiang
collection PubMed
description Due to its low stiffness, the boring bar used in deep-hole-boring is prone to violent vibration during the cutting process. It is often inaccurate and inefficient to judge the vibration state of the boring bar through artificial experience. To detect the change of the vibration state of the boring bar over time, guide the adjustment of the processing parameters, and avoid wastage of the workpiece and the loss of equipment, it is particularly important to intelligently monitor the vibration state of the boring bar during processing. In this paper, the boring bar is taken as the research object, and an intelligent monitoring technology of the boring bar’s vibration state based on deep learning is proposed. Based on grouping convolution, channel shuffle, and BiLSTM, a shuffle-BiLSTM NET model is constructed, which is both lightweight and has a high classification accuracy. The boring experiment platform is built, and 192 groups of cutting experiments are carried out. The three-way acceleration and sound pressure signals are collected, and the signals are processed by smoothed pseudo-Wigner–Ville distribution. The original signals are transformed into a 256 × 256 × 3 matrix obtained by a two-dimensional time–frequency spectrum diagram. The matrix is input into the model to recognize the boring bar’s vibration state. The final classification accuracy is 91.2%. A variety of typical deep learning models are introduced for performance comparison, which proves the superiority of the models and methods used in this paper.
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spelling pubmed-103472812023-07-15 Research on Intelligent Monitoring of Boring Bar Vibration State Based on Shuffle-BiLSTM Liu, Qiang Li, Dingkun Ma, Jing Bai, Zhengyan Liu, Jiaqi Sensors (Basel) Article Due to its low stiffness, the boring bar used in deep-hole-boring is prone to violent vibration during the cutting process. It is often inaccurate and inefficient to judge the vibration state of the boring bar through artificial experience. To detect the change of the vibration state of the boring bar over time, guide the adjustment of the processing parameters, and avoid wastage of the workpiece and the loss of equipment, it is particularly important to intelligently monitor the vibration state of the boring bar during processing. In this paper, the boring bar is taken as the research object, and an intelligent monitoring technology of the boring bar’s vibration state based on deep learning is proposed. Based on grouping convolution, channel shuffle, and BiLSTM, a shuffle-BiLSTM NET model is constructed, which is both lightweight and has a high classification accuracy. The boring experiment platform is built, and 192 groups of cutting experiments are carried out. The three-way acceleration and sound pressure signals are collected, and the signals are processed by smoothed pseudo-Wigner–Ville distribution. The original signals are transformed into a 256 × 256 × 3 matrix obtained by a two-dimensional time–frequency spectrum diagram. The matrix is input into the model to recognize the boring bar’s vibration state. The final classification accuracy is 91.2%. A variety of typical deep learning models are introduced for performance comparison, which proves the superiority of the models and methods used in this paper. MDPI 2023-07-03 /pmc/articles/PMC10347281/ /pubmed/37447972 http://dx.doi.org/10.3390/s23136123 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Qiang
Li, Dingkun
Ma, Jing
Bai, Zhengyan
Liu, Jiaqi
Research on Intelligent Monitoring of Boring Bar Vibration State Based on Shuffle-BiLSTM
title Research on Intelligent Monitoring of Boring Bar Vibration State Based on Shuffle-BiLSTM
title_full Research on Intelligent Monitoring of Boring Bar Vibration State Based on Shuffle-BiLSTM
title_fullStr Research on Intelligent Monitoring of Boring Bar Vibration State Based on Shuffle-BiLSTM
title_full_unstemmed Research on Intelligent Monitoring of Boring Bar Vibration State Based on Shuffle-BiLSTM
title_short Research on Intelligent Monitoring of Boring Bar Vibration State Based on Shuffle-BiLSTM
title_sort research on intelligent monitoring of boring bar vibration state based on shuffle-bilstm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10347281/
https://www.ncbi.nlm.nih.gov/pubmed/37447972
http://dx.doi.org/10.3390/s23136123
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