Individualized Prediction of In-Plane Shear Stress-Strain Curves for Composites Using Early-Stage Digital Image Correlation Strain Fields.

Chongyu Ruan, Maowen Yao, Xiangyu Zhao, Zhisheng Yu, Guangwu Fang

Abstract read

In one paragraph

Article in Materials (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Chongyu RuanCollege of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Liyang 213300, China.

Maowen YaoCollege of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Liyang 213300, China.

Xiangyu ZhaoCollege of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Liyang 213300, China.

Zhisheng YuCollege of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Liyang 213300, China.

Guangwu FangCollege of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Liyang 213300, China.ORCID 0000-0003-2963-4624

Funding

Key Laboratory of Inlet and Exhaust Technology, Ministry of Education CEPE2024014

6 · The paper itself

Abstract

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 R

Indexed as

CFRPconvolutional neural networknon-destructive evaluationproperty scatterV-notched shear test

Identifiers

PMID42355191

DOI10.3390/ma19122609

PMCPMC13303929

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.