Electromechanical Impedance Data-Driven Metal Structural Tensile Stress Identification Using Generative Adversarial Networks.
Saved in:
| Title: | Electromechanical Impedance Data-Driven Metal Structural Tensile Stress Identification Using Generative Adversarial Networks. |
|---|---|
| Authors: | Ai, Demi1 (AUTHOR) aidemi@hust.edu.cn, Zhang, Rui1 (AUTHOR) |
| Source: | Materials (1996-1944). Jun2026, Vol. 19 Issue 12, p2445. 28p. |
| Subjects: | Generative adversarial networks, Data augmentation, Piezoelectric transducers, Electromechanical impedance analysis, Tensile tests, Deep learning, Convolutional neural networks |
| Abstract: | Deep learning networks facilitate automated metal material/structural stress identification when employing the electromechanical impedance/admittance (EMI/EMA) of piezoelectric ceramic (PZT) transducers, while insufficient data quantity and low quality usually restrict the performance of data-driven deep networks. To address this problem, this paper innovatively proposed an original data enhancement method using the EMA generative adversarial network (EMAGAN) to overcome measurement data inefficiency and deficiency for deep learning-based stress identification, which is difficult to accomplish using the traditional EMA technique. In this method, a novel data-normalized algorithm was tuned to collaboratively foster the EMAGAN-based dataset generation. Then, the synthetic datasets incorporated with original ones were fed into an adaptively established one-dimensional convolutional neural network (1DCNN) for accurate stress prediction. A validating experiment was performed on an aluminum beam specimen subjected to uniaxial tensile load until failure, which was continuously monitored via two surface-bonded PZT transducers. The efficacy of the generated EMA datasets was evaluated through comparison with the raw ones in terms of statistical errors and deep learning-based aluminum structural stress identification. The results demonstrated that the EMAGAN generated high-accuracy EMA data which exceeded 380 times that of the normal collection method, and the EMAGAN paired with 1DCNN provides a promising way for EMA data-driven metal structural stress identification with high efficiency, intelligence and accuracy. [ABSTRACT FROM AUTHOR] |
| Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Deep learning networks facilitate automated metal material/structural stress identification when employing the electromechanical impedance/admittance (EMI/EMA) of piezoelectric ceramic (PZT) transducers, while insufficient data quantity and low quality usually restrict the performance of data-driven deep networks. To address this problem, this paper innovatively proposed an original data enhancement method using the EMA generative adversarial network (EMAGAN) to overcome measurement data inefficiency and deficiency for deep learning-based stress identification, which is difficult to accomplish using the traditional EMA technique. In this method, a novel data-normalized algorithm was tuned to collaboratively foster the EMAGAN-based dataset generation. Then, the synthetic datasets incorporated with original ones were fed into an adaptively established one-dimensional convolutional neural network (1DCNN) for accurate stress prediction. A validating experiment was performed on an aluminum beam specimen subjected to uniaxial tensile load until failure, which was continuously monitored via two surface-bonded PZT transducers. The efficacy of the generated EMA datasets was evaluated through comparison with the raw ones in terms of statistical errors and deep learning-based aluminum structural stress identification. The results demonstrated that the EMAGAN generated high-accuracy EMA data which exceeded 380 times that of the normal collection method, and the EMAGAN paired with 1DCNN provides a promising way for EMA data-driven metal structural stress identification with high efficiency, intelligence and accuracy. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 19961944 |
| DOI: | 10.3390/ma19122445 |
