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Assessment of a three-axis on-rotor sensing performance for machining process monitoring: a case study

Online monitoring of cutting conditions is essential in intelligent manufacturing, and vibrations are one of the most effective signals in monitoring machining conditions. Generally, traditional wired accelerometers should be installed on a motionless or stable platform, such as a tool holder or lat...

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Autores principales: Li, Chun, Zou, Zhexiang, Lu, Kaibo, Wang, Hongjun, Cattley, Robert, Ball, Andrew D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9546854/
https://www.ncbi.nlm.nih.gov/pubmed/36207603
http://dx.doi.org/10.1038/s41598-022-21415-w
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author Li, Chun
Zou, Zhexiang
Lu, Kaibo
Wang, Hongjun
Cattley, Robert
Ball, Andrew D.
author_facet Li, Chun
Zou, Zhexiang
Lu, Kaibo
Wang, Hongjun
Cattley, Robert
Ball, Andrew D.
author_sort Li, Chun
collection PubMed
description Online monitoring of cutting conditions is essential in intelligent manufacturing, and vibrations are one of the most effective signals in monitoring machining conditions. Generally, traditional wired accelerometers should be installed on a motionless or stable platform, such as a tool holder or lathe bed, to sense vibrations. Such installation methods would cause the signals to suffer more serious noise interferences and a low signal-to-noise ratio, resulting in less sensitivity to valuable information. Therefore, this study developed a novel three-axis wireless on-rotor sensing (ORS) system for monitoring the turning process. The Micro Electromechanical System (MEMS) accelerometer sensor node can be mounted on a rotating workpiece or spindle rotor and is more sensitive in detecting the vibrations of the entire rotor system without any modification of the lathe system and interference in the cutting procedure. The processor, data acquisition, and Bluetooth Low Energy (BLE) 5.0+ modules were developed and debugged to cooperate with a piezoelectric triaxial accelerometer, with a vibration amplitude not larger than ± 16 g. A series of turning tests were conducted and the results were compared with those from the commercial wired accelerometers, which proved that the ORS system can measure the vibration signal of the rotor system more effectively and sensitively than wired accelerometers, thus demonstrating the accurate monitoring of machining parameters.
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spelling pubmed-95468542022-10-09 Assessment of a three-axis on-rotor sensing performance for machining process monitoring: a case study Li, Chun Zou, Zhexiang Lu, Kaibo Wang, Hongjun Cattley, Robert Ball, Andrew D. Sci Rep Article Online monitoring of cutting conditions is essential in intelligent manufacturing, and vibrations are one of the most effective signals in monitoring machining conditions. Generally, traditional wired accelerometers should be installed on a motionless or stable platform, such as a tool holder or lathe bed, to sense vibrations. Such installation methods would cause the signals to suffer more serious noise interferences and a low signal-to-noise ratio, resulting in less sensitivity to valuable information. Therefore, this study developed a novel three-axis wireless on-rotor sensing (ORS) system for monitoring the turning process. The Micro Electromechanical System (MEMS) accelerometer sensor node can be mounted on a rotating workpiece or spindle rotor and is more sensitive in detecting the vibrations of the entire rotor system without any modification of the lathe system and interference in the cutting procedure. The processor, data acquisition, and Bluetooth Low Energy (BLE) 5.0+ modules were developed and debugged to cooperate with a piezoelectric triaxial accelerometer, with a vibration amplitude not larger than ± 16 g. A series of turning tests were conducted and the results were compared with those from the commercial wired accelerometers, which proved that the ORS system can measure the vibration signal of the rotor system more effectively and sensitively than wired accelerometers, thus demonstrating the accurate monitoring of machining parameters. Nature Publishing Group UK 2022-10-07 /pmc/articles/PMC9546854/ /pubmed/36207603 http://dx.doi.org/10.1038/s41598-022-21415-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Li, Chun
Zou, Zhexiang
Lu, Kaibo
Wang, Hongjun
Cattley, Robert
Ball, Andrew D.
Assessment of a three-axis on-rotor sensing performance for machining process monitoring: a case study
title Assessment of a three-axis on-rotor sensing performance for machining process monitoring: a case study
title_full Assessment of a three-axis on-rotor sensing performance for machining process monitoring: a case study
title_fullStr Assessment of a three-axis on-rotor sensing performance for machining process monitoring: a case study
title_full_unstemmed Assessment of a three-axis on-rotor sensing performance for machining process monitoring: a case study
title_short Assessment of a three-axis on-rotor sensing performance for machining process monitoring: a case study
title_sort assessment of a three-axis on-rotor sensing performance for machining process monitoring: a case study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9546854/
https://www.ncbi.nlm.nih.gov/pubmed/36207603
http://dx.doi.org/10.1038/s41598-022-21415-w
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