Individualized Prediction of In-Plane Shear Stress–Strain Curves for Composites Using Early-Stage Digital Image Correlation Strain Fields.
Saved in:
| Title: | Individualized Prediction of In-Plane Shear Stress–Strain Curves for Composites Using Early-Stage Digital Image Correlation Strain Fields. |
|---|---|
| Authors: | Ruan, Chongyu1 (AUTHOR), Yao, Maowen1,2 (AUTHOR), Zhao, Xiangyu1 (AUTHOR), Yu, Zhisheng1,2 (AUTHOR), Fang, Guangwu1,2 (AUTHOR) fgwu89424@nuaa.edu.cn |
| Source: | Materials (1996-1944). Jun2026, Vol. 19 Issue 12, p2609. 22p. |
| Subjects: | Digital image correlation, Stress-strain curves, Composite materials, Carbon fiber-reinforced plastics, Data augmentation, Structural health monitoring, Convolutional neural networks |
| Abstract: | Highlights: Single early DIC strain map predicts full CFRP shear stress–strain curve. CNN maps 0.2% strain field to full curve with overall R2 = 0.945. Data augmentation and Dropout reduce RMSE by 40% vs. baseline. Individual-specific scatter captured (min R2 = 0.821, max = 0.992). The in-plane shear performance of carbon fiber-reinforced polymer (CFRP) composites is critical for structural design but is challenged by significant property scatter. This study aims to achieve individualized prediction of the complete shear stress–strain curve for each composite specimen using only a single early-stage digital image correlation (DIC) strain field. Systematic in-plane shear tests were conducted on 45 laminated carbon fiber/epoxy specimens with synchronized full-field DIC data and macroscopic load–displacement records. A lightweight encoder–decoder convolutional neural network was developed, taking a single DIC strain contour map at 0.2% global strain as input and mapping it directly to the full-range stress–strain curve up to failure for that specific specimen. Data augmentation and Dropout regularization mitigated the small-sample challenge. The proposed model achieved strong predictive performance across the five-fold cross-validation yielded a mean R2 of 0.926 ± 0.022 and a mean RMSE of 6.37 ± 1.14 MPa for stress. Individual specimen predictions on the test set yielded an average R2 of 0.945, with a minimum of 0.821, confirming robust capability across scattered properties. Residual analysis elucidated error characteristics across deformation stages. This research provides a novel paradigm for non-destructive, early-stage individualized assessment of composite mechanical properties, with applications in structural health monitoring and probabilistic design. [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: | Highlights: Single early DIC strain map predicts full CFRP shear stress–strain curve. CNN maps 0.2% strain field to full curve with overall R2 = 0.945. Data augmentation and Dropout reduce RMSE by 40% vs. baseline. Individual-specific scatter captured (min R2 = 0.821, max = 0.992). The in-plane shear performance of carbon fiber-reinforced polymer (CFRP) composites is critical for structural design but is challenged by significant property scatter. This study aims to achieve individualized prediction of the complete shear stress–strain curve for each composite specimen using only a single early-stage digital image correlation (DIC) strain field. Systematic in-plane shear tests were conducted on 45 laminated carbon fiber/epoxy specimens with synchronized full-field DIC data and macroscopic load–displacement records. A lightweight encoder–decoder convolutional neural network was developed, taking a single DIC strain contour map at 0.2% global strain as input and mapping it directly to the full-range stress–strain curve up to failure for that specific specimen. Data augmentation and Dropout regularization mitigated the small-sample challenge. The proposed model achieved strong predictive performance across the five-fold cross-validation yielded a mean R2 of 0.926 ± 0.022 and a mean RMSE of 6.37 ± 1.14 MPa for stress. Individual specimen predictions on the test set yielded an average R2 of 0.945, with a minimum of 0.821, confirming robust capability across scattered properties. Residual analysis elucidated error characteristics across deformation stages. This research provides a novel paradigm for non-destructive, early-stage individualized assessment of composite mechanical properties, with applications in structural health monitoring and probabilistic design. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 19961944 |
| DOI: | 10.3390/ma19122609 |
