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


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
07 Oct 2022
Historique:
received: 30 06 2022
accepted: 27 09 2022
entrez: 7 10 2022
pubmed: 8 10 2022
medline: 8 10 2022
Statut: epublish

Résumé

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.

Identifiants

pubmed: 36207603
doi: 10.1038/s41598-022-21415-w
pii: 10.1038/s41598-022-21415-w
pmc: PMC9546854
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

16813

Subventions

Organisme : Science and Technology Planning Project of Guangdong Province
ID : 2020KTSCX188
Organisme : National Natural Science Foundation of China
ID : 52175108
Organisme : Beijing Municipal Science and Technology Commission
ID : Z201100008320004

Informations de copyright

© 2022. The Author(s).

Références

Sensors (Basel). 2016 Feb 23;16(3):269
pubmed: 26907297
Sensors (Basel). 2018 Apr 18;18(4):
pubmed: 29670062
Sensors (Basel). 2020 Dec 26;21(1):
pubmed: 33375340

Auteurs

Chun Li (C)

School of Industrial Automation, Beijing Institute of Technology, Zhuhai, 519088, People's Republic of China.
Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, HD1 3DH, UK.

Zhexiang Zou (Z)

School of Industrial Automation, Beijing Institute of Technology, Zhuhai, 519088, People's Republic of China. zhexiang.zou@hud.ac.uk.
Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, HD1 3DH, UK. zhexiang.zou@hud.ac.uk.

Kaibo Lu (K)

College of Mechanical Engineering, Taiyuan University of Technology, Shanxi, 030024, People's Republic of China.

Hongjun Wang (H)

School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing, 100192, People's Republic of China.

Robert Cattley (R)

Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, HD1 3DH, UK.

Andrew D Ball (AD)

Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, HD1 3DH, UK.

Classifications MeSH