Mechanical Sciences
Mechanical Sciences
Mechanical Sciences
Articles | Volume 13, issue 1
Articles | Volume 13, issue 1
https://doi.org/10.5194/ms-13-291-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ms-13-291-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 1
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

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Subject: Machining and Manufacturing Processes | Techniques and Approaches: Reliability and Probability Analysis
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Cited articles

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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.
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