Evidence map›Paper›PMID 40219237›Full record

ArticlePolymers2025

Theoretical Prediction Method for Tensile Properties of High-Strength Steel/Carbon Fiber-Reinforced Polymer Laminates.

Haichao Hu, Qiang Wei, Tianao Wang, Quanjin Ma, Shupeng Pan, Fengqi Li, Chuancai Wang, Jie Ding

Abstract read
In one paragraph

Article in Polymers, 2025. 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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1 · What the graph read from it

What it found

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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

The trial behind it

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3 · Its place in the literature

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Haichao HuSchool of Mechanical Engineering, Tianjin Sino-German University of Applied Sciences, Tianjin 300350, China.ORCID 0000-0003-3143-8382
Qiang WeiSchool of Materials Science and Engineering, Hebei University of Technology, Tianjin 300401, China.ORCID 0000-0002-4620-5269
Tianao WangSchool of Mechanical Engineering, Tianjin Sino-German University of Applied Sciences, Tianjin 300350, China.
Quanjin MaSchool of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055, China.ORCID 0000-0002-0973-4794
Shupeng PanSchool of Mechanical Engineering, Tianjin Sino-German University of Applied Sciences, Tianjin 300350, China.
Fengqi LiSchool of Mechanical Engineering, Tianjin Sino-German University of Applied Sciences, Tianjin 300350, China.
Chuancai WangSchool of Mechanical Engineering, Tianjin Sino-German University of Applied Sciences, Tianjin 300350, China.
Jie DingSchool of Mechanical Engineering, Tianjin Sino-German University of Applied Sciences, Tianjin 300350, China.

Funding

The Science&Technology Development Fund of Tianjin Education Commission for Higher Education 2021KJ099
6 · The paper itself

Abstract

This study introduces a method for predicting the tensile properties of high-strength steel/carbon fiber-reinforced polymer (CFRP) composite laminates using Metal Volume Fraction (MVF) theory. DP590 and DP980 high-strength steels (thickness ~0.8 mm) were selected as substrates, and composite laminates were fabricated by compression molding with CFRP prepreg. Tensile tests were performed on an MTS universal testing machine, and fracture morphology was analyzed using scanning electron microscopy (SEM). The results demonstrated a typical mixed failure mode: necking and fracture in the metal layer, and neat fiber fracture in the CFRP layer. Comparisons of experimental tensile strength with theoretical predictions revealed that the model based on the metal strength at fracture significantly outperformed the model using tensile strength for predictions, with narrower error ranges. For example, the error for DP590/CFRP laminates ranged from 2.31% to 12.89%, whereas for DP980/CFRP laminates, it was -6.12%. Additionally, the study showed that using metals with higher plasticity in fiber metal laminates could underutilize the metal layer's potential at peak stress, leading to significant deviations when predictions rely on tensile strength. Therefore, it is recommended to use the metal strength corresponding to peak stress for more accurate MVF-based tensile property predictions. This method provides a robust theoretical foundation for predicting the tensile performance of high-strength steel/CFRP laminates, aiding in optimizing structural designs for automotive and aerospace applications. Future research could explore the effects of different metal and fiber combinations, as well as more complex stacking designs.

Indexed as

composite laminatemechanical propertiesMetal Volume Fraction theorynumerical simulationprediction

Identifiers

PMID40219237
PMCPMC11991359

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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.