Determination of Clamping Force Using Bolt Vibration Responses during the Tightening Processopen access
- Authors
- Toh, Gyungmin; Gwon, Jaesoo; Park, Junhong
- Issue Date
- Dec-2019
- Publisher
- MDPI
- Keywords
- bolt joints; clamping force; vibration; machine learning
- Citation
- APPLIED SCIENCES-BASEL, v.9, no.24, pp.1 - 12
- Indexed
- SCIE
SCOPUS
- Journal Title
- APPLIED SCIENCES-BASEL
- Volume
- 9
- Number
- 24
- Start Page
- 1
- End Page
- 12
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/11630
- DOI
- 10.3390/app9245379
- Abstract
- Featured Application This approach allows detection of transient spectral characteristics using the cepstrum for diagnosis. Abstract This paper presents a novel method to measure clamping force by using the vibration of bolts. The resonance frequency of the bolt increases in line with the clamping force during the tightening process. These characteristics were measured and utilized in the k-means clustering algorithm. Bolt specimens were fastened to the load cell using a nutrunner for verification of the proposed method. The precisely measured clamping force was labeled. The labeled data was used to predict the clamping force from the vibration responses. To use the proposed method in assembly of actual parts, an accelerometer was attached to the nutrunner for vibration measurements. This enabled continuous monitoring of the clamping force without influence on the parts. The estimated clamping force was compared with those from the torque method. When the vibration of a bolt was transmitted through the nutrunner, loss of high-frequency vibration energy occurred. The resonant frequency band vibrations of the bolt were preserved to determine the fastening force. The components in the low frequency band were excluded using a band-pass filter. The clamping force of the bolt used in the vehicle's lower arm and the link was also evaluated precisely. By using the proposed method, it is possible to continuously monitor variations of the clamping force during the manufacturing process.
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