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Advanced Composite Materials and Structures: Forming, Characterization and Simulation Enhanced with AI

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Advanced Composites".

Deadline for manuscript submissions: 20 April 2026 | Viewed by 723

Special Issue Editors

National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: composite mechanics; impact engineering; machine learning; multiscale modeling
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Guest Editor
Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan
Interests: thin-walled structures; numerical simulations; forming process; characterization; laminates; composites

Special Issue Information

Dear Colleagues,

Carbon fiber-reinforced composites, known for their lightweight, high strength, and corrosion resistance, are widely used in aerospace, automotive, wind energy, and sports equipment industries. In modern engineering, leveraging simulation, characterization, and modeling techniques to study the performance of carbon fiber composites has become a critical approach for design and optimization. Finite element analysis (FEA) and other numerical simulation methods enable engineers to predict the mechanical behavior of composites under various load conditions. These simulations not only help optimize the material's layer structure but also reduce the number and cost of physical experiments. In material characterization, advanced microscopic imaging techniques and Raman spectroscopy are used to investigate the microstructure and interfacial properties of carbon fiber composites, providing reliable data for model development. Furthermore, in the field of simulation, multiscale modeling techniques allow researchers to analyze materials’ behavior comprehensively, from the microscopic fiber–matrix interface to the macroscopic component level, accurately predicting their performance in real-world applications. The integration of these techniques not only enhances the development efficiency of carbon fiber composites but also drives innovative applications across broader domains. In the future, with the incorporation of artificial intelligence, artificial neural network, and big data technologies, these research methods will become more intelligent, further advancing the performance optimization and industrial application of carbon fiber composites.

Dr. Jun Feng
Dr. Xiaodong Zhao
Guest Editors

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Keywords

  • composite materials
  • multi-objective optimization
  • artificial intelligence
  • machine learning
  • CFRP laminates

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Published Papers (1 paper)

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Research

25 pages, 46031 KB  
Article
Cross-Scale Modeling of CFRP Stacking Sequence in Filament-Wound Composite Pressure Vessels: In-Plane and Inter-Layer Homogenization Analysis
by Ziqi Wang, Ji Shi, Xiaodong Zhao, Hui Li, Huiming Shen, Jianguo Liang and Jun Feng
Materials 2025, 18(19), 4612; https://doi.org/10.3390/ma18194612 - 5 Oct 2025
Viewed by 501
Abstract
Composite pressure vessels have attracted significant attention in recent years owing to their lightweight characteristics and superior mechanical performance. However, analyzing composite layers remains challenging due to complex filament-winding (FW) pattern structures and the associated high computational costs. This study introduces a homogenization [...] Read more.
Composite pressure vessels have attracted significant attention in recent years owing to their lightweight characteristics and superior mechanical performance. However, analyzing composite layers remains challenging due to complex filament-winding (FW) pattern structures and the associated high computational costs. This study introduces a homogenization method to achieve cross-scale modeling of carbon fiber-reinforced plastic (CFRP) layers, accounting for both lay-up sequence and in-plane FW diamond-shaped form. The stacking sequence in an FW Type IV composite pressure vessel is numerically investigated through ply modeling and cross-scale homogenization. The composite tank structure, featuring a polyamide PA66 liner, is designed for a working pressure of 70 MPa and comprises 12 helical winding layers and 17 hoop winding layers. An FW cross-undulation representative volume element (RVE) is developed based on actual in-plane mesostructures, suggesting an equivalent laminate RVE effective elastic modulus. Furthermore, six different lay-up sequences are numerically compared using ply models and fully and partially homogenized models. The structural displacements in both radial and axial directions are validated across all modeling approaches. The partial homogenization method successfully captures the detailed fiber-direction stress distribution in the innermost two hoop or helical layers. By applying the Hashin tensile failure criterion, the burst pressure of the composite tank is evaluated, revealing 7.56% deviation between the partial homogenization model and the ply model. Fatigue life analysis of the Type IV composite pressure vessel is conducted using ABAQUS® coupled with FE-SAFE, incorporating an S-N curve for polyamide PA66. The results indicate that the fatigue cycles of the liner exhibit only 0.28% variation across different stacking sequences, demonstrating that homogenization has a negligible impact on liner lifecycle predictions. The proposed cross-scale modeling framework offers an effective approach for multiscale simulation of FW composite pressure vessels, balancing computational efficiency with accuracy. Full article
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