Development of Maximum Residual Stress Prediction Technique for Shot-Peened Specimen Using Rayleigh Wave Dispersion Data Based on Convolutional Neural Network.
- DOI
- 10.3390/ma16237406
- Published
- 2023-11-28
- Container
- Materials (Basel)
- Publisher
- Not recorded
- Open access
- yes
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BibTeX
@article{allodium:10.3390/ma16237406,
title = {Development of Maximum Residual Stress Prediction Technique for Shot-Peened Specimen Using Rayleigh Wave Dispersion Data Based on Convolutional Neural Network.},
author = {Choi YW and Lee TG and Yeom YT and Kwon SD and Kim HH and Lee KY and Kim HJ and Song SJ.},
year = {2023},
journal = {Materials (Basel)},
doi = {10.3390/ma16237406},
url = {https://doi.org/10.3390/ma16237406}
}RIS
TY - JOUR TI - Development of Maximum Residual Stress Prediction Technique for Shot-Peened Specimen Using Rayleigh Wave Dispersion Data Based on Convolutional Neural Network. AU - Choi YW AU - Lee TG AU - Yeom YT AU - Kwon SD AU - Kim HH AU - Lee KY AU - Kim HJ AU - Song SJ. PY - 2023 JO - Materials (Basel) DO - 10.3390/ma16237406 UR - https://doi.org/10.3390/ma16237406 ER -
APA
YW, C., TG, L., YT, Y., SD, K., HH, K., KY, L., HJ, K., & SJ., S. (2023). Development of Maximum Residual Stress Prediction Technique for Shot-Peened Specimen Using Rayleigh Wave Dispersion Data Based on Convolutional Neural Network.. Materials (Basel). https://doi.org/10.3390/ma16237406
Source records
- europe-pmc · retrieved 2026-09-27T04:31:58.780Z