This study introduces a cutting-edge approach for achieving precise non-contact characterization of material hardness by integrating electromagnetic acoustic resonance (EMAR) with a one-dimensional convolutional neural network (1D-CNN). EMAR is strategically utilized to address the challenge of low energy conversion efficiency in electromagnetic ultrasonic transducers for non-contact measurements. A 1D-CNN-based neural network is proposed, designed to dynamically extract features from the original signals and employ classification and regression techniques to directly forecast variations in material hardness. Furthermore, EMAR signals are meticulously compared to pinpoint the optimal input featuring specific resonant frequencies to enhance model performance. The viability of the proposed method is rigorously validated through experimentation on metallic specimens subjected to diverse heat treatments. The results underscore the efficacy of this approach in discerning alterations in material hardness induced by heat treatments, all achieved in a noninvasive manner.
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28 April 2025
Research Article|
April 29 2025
Non-contact characterization of material hardness by deep learning-assisted electromagnetic acoustic resonance method

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Jinshan Wen;
Jinshan Wen
(Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – original draft)
1
School of Aerospace Engineering, Xiamen University
, 422, South Siming Road, Xiamen 361005, China
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Mingxi Deng
;
Mingxi Deng
a)
(Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Validation)
2
College of Aerospace Engineering, Chongqing University
, Chongqing 400044, China
Search for other works by this author on:
Jinshan Wen
1
Mingxi Deng
2,a)
Weibin Li
1,a)
1
School of Aerospace Engineering, Xiamen University
, 422, South Siming Road, Xiamen 361005, China
2
College of Aerospace Engineering, Chongqing University
, Chongqing 400044, China
Appl. Phys. Lett. 126, 174102 (2025)
Article history
Received:
February 13 2025
Accepted:
April 08 2025
Connected Content
A companion article has been published:
Measuring material hardness without contact
Citation
Jinshan Wen, Mingxi Deng, Weibin Li; Non-contact characterization of material hardness by deep learning-assisted electromagnetic acoustic resonance method. Appl. Phys. Lett. 28 April 2025; 126 (17): 174102. https://doi.org/10.1063/5.0265254
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