Articles | Volume 13, issue 1
https://doi.org/10.5194/ms-13-291-2022
https://doi.org/10.5194/ms-13-291-2022
Short communication
 | 
23 Mar 2022
Short communication |  | 23 Mar 2022

Short communication: A case study of stress monitoring with non-destructive stress measurement and deep learning algorithms

Yaofeng Ji, Qingbo Lu, and Qingyu Yao

Viewed

Total article views: 764 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
616 138 10 764 8 9
  • HTML: 616
  • PDF: 138
  • XML: 10
  • Total: 764
  • BibTeX: 8
  • EndNote: 9
Views and downloads (calculated since 23 Mar 2022)
Cumulative views and downloads (calculated since 23 Mar 2022)

Viewed (geographical distribution)

Total article views: 684 (including HTML, PDF, and XML) Thereof 684 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 28 Mar 2024
Download
Short summary
During manufacturing, machine failure can lead to fatal damage to both people and the environment, and non-destructive stress measurement is necessary to provide safety maintenance. This paper focuses on the feasibility and capability of deep learning algorithms in stress prediction using Barkhausen noise signals. Our findings pave the way for the implementation of auto-detection devices for metal materials in on-site manufacturing processes.