Articles | Volume 13, issue 1
Mech. Sci., 13, 291–296, 2022
https://doi.org/10.5194/ms-13-291-2022
Mech. Sci., 13, 291–296, 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 et al.

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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.