Articles | Volume 13, issue 1
Mech. Sci., 13, 291–296, 2022
Mech. Sci., 13, 291–296, 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.

Related subject area

Subject: Machining and Manufacturing Processes | Techniques and Approaches: Reliability and Probability Analysis
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Estimation of tool life and cutting burr in high speed milling of the compacted graphite iron by DE based adaptive neuro-fuzzy inference system
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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.