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Interrelation between mechanical and electromagnetic radiation emission parameters with variable notch-width ratios under tensile fracture in silicon steel

  • Anu Anand ORCID logo EMAIL logo and Rajeev Kumar
Published/Copyright: December 18, 2024
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Abstract

The research investigates the effect of variable notch-width ratios on mechanical and electromagnetic radiation (EMR) parameters during the tensile fracture of silicon steel. The primary purpose is to comprehend the plastic deformation and crack propagation at an atomic level. The mechanical and EMR parameters chosen for analysis were correlated. The EMR energy release rate correlated well with the elastic strain energy release rate. The good fit between the EMR parameters and the plastic zone radius is a novel method to determine the crack growth behaviour of metals. A theoretical model of dislocation predicted the nature of EMR signals at fracture. A reasonable agreement between cross-slip energy with maximum stress at crack instability, the elastic strain energy release rate, and the EMR energy release rate will help assess the dislocation dynamics of metals. The EMR amplitude was sensitive to varying strain rates.


Corresponding author: Anu Anand, Department of Mechanical Engineering, Birla Institute of Technology, Mesra, Ranchi, 835215, India, E-mail:

Acknowledgments

The authors would like to thank all the Central Instrumentation Facility (CIF) lab personnel at the Birla Institute of Technology, Mesra, Ranchi, for assisting in conducting the EMR experiments and SEM analysis.

  1. Research ethics: Not applicable.

  2. Informed consent: Informed consent was obtained from all individuals included in this study.

  3. Author contributions: Anu Anand was involved in investigations, experimental analysis, and the preparation of the original draft. Rajeev Kumar conceptualized the methodology and supervised the manuscript’s writing, reviewing, and editing. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The author states no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: All data generated or analyzed during this study are included in this published article.

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Received: 2024-02-11
Accepted: 2024-06-14
Published Online: 2024-12-18
Published in Print: 2024-11-26

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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