Chao Zhang , Zhouyang Bian , Tinh Quoc Bui , Jose L Curiel-Sosa
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引用次数: 0
Abstract
Textile composite structures in specific engineering applications can face safety concerns arising from exposure to high temperatures and off-axis loadings. High-fidelity finite element (FE) simulations and analytical models are both labor-intensive and time-consuming when predicting the mechanical behavior of textile composites under such loadings. To address this challenge, we develop a tree-based machine learning (ML) surrogate model for predicting the off-axis mechanical properties of warp-reinforced 2.5D woven composites in high temperature environments. To this setting, the tensile modulus and strength can be directly obtained based on the given temperature and off-axis angle, and the predicted results are in good agreement with FE simulations solutions. This study is expected to offer novel insights for the development of early warning systems that monitor abnormal temperatures and off-axis loadings in textile composite structures.
期刊介绍:
The past few decades have seen outstanding advances in the use of composite materials in structural applications. There can be little doubt that, within engineering circles, composites have revolutionised traditional design concepts and made possible an unparalleled range of new and exciting possibilities as viable materials for construction. Composite Structures, an International Journal, disseminates knowledge between users, manufacturers, designers and researchers involved in structures or structural components manufactured using composite materials.
The journal publishes papers which contribute to knowledge in the use of composite materials in engineering structures. Papers deal with design, research and development studies, experimental investigations, theoretical analysis and fabrication techniques relevant to the application of composites in load-bearing components for assemblies, ranging from individual components such as plates and shells to complete composite structures.