Process-driven strength enhancement and progressive damage analysis of three-dimensional woven composites traction rods

TY - JOUR

T1 - Process-driven strength enhancement and progressive damage analysis of three-dimensional woven composites traction rods

AU - Huang, Hao

AU - Shan, Zhongde

AU - Xing, Yanming

AU - Guo, Zitong

AU - Yang, Chunguang

AU - Liu, Jianhua

AU - Sun, Zheng

AU - Ao, Xiaohui

AU - Wang, Dong

AU - Tan, Chenchen

AU - Wang, Weihao

AU - Luo, Juncheng

N1 - Publisher Copyright: © 2025 The Authors

PY - 2025/3

Y1 - 2025/3

N2 - With the growing emphasis on lightweight and high-strength materials, optimizing the performance of composites has become increasingly critical for engineering applications. This study presents a comprehensive methodology for optimizing the process parameters of three-dimensional (3D) woven composites traction rods using a progressive damage failure model and an embedding feature selection-based back propagation neural network (EFSBPNN) surrogate model. The forming processes were systematically analyzed to identify key design variables, followed by developing an advanced surrogate model integrating material, structural, and process parameters. A progressive damage failure analysis model was established to predict the structural response, forming the basis for the optimization strategy. Experimental validation demonstrated significant performance improvements, including an 18.56% increase in stiffness, a 40.69% enhancement in strength, and a 6.1% reduction in mass. The surrogate model achieved high accuracy across multiple metrics, and the predictive capabilities of the damage model showed a relative error of 13.5% for stiffness and 10.7% for strength. These findings highlight the potential of the proposed approach for the efficient design and optimization of composite structures.

AB - With the growing emphasis on lightweight and high-strength materials, optimizing the performance of composites has become increasingly critical for engineering applications. This study presents a comprehensive methodology for optimizing the process parameters of three-dimensional (3D) woven composites traction rods using a progressive damage failure model and an embedding feature selection-based back propagation neural network (EFSBPNN) surrogate model. The forming processes were systematically analyzed to identify key design variables, followed by developing an advanced surrogate model integrating material, structural, and process parameters. A progressive damage failure analysis model was established to predict the structural response, forming the basis for the optimization strategy. Experimental validation demonstrated significant performance improvements, including an 18.56% increase in stiffness, a 40.69% enhancement in strength, and a 6.1% reduction in mass. The surrogate model achieved high accuracy across multiple metrics, and the predictive capabilities of the damage model showed a relative error of 13.5% for stiffness and 10.7% for strength. These findings highlight the potential of the proposed approach for the efficient design and optimization of composite structures.

KW - 3D woven composites

KW - Process optimization

KW - Progressive damage

KW - Traction rods

UR - https://www.scopus.com/pages/publications/85215861268

U2 - 10.1016/j.matdes.2025.113617

DO - 10.1016/j.matdes.2025.113617

M3 - Article

AN - SCOPUS:85215861268

SN - 0264-1275

VL - 251

JO - Materials and Design

JF - Materials and Design

M1 - 113617

ER -