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A novel multi-objective optimization framework for tensile properties of carbon/Kevlar fiber-reinforced composite laminates based on improved hybrid SPEA2-MOPSO algorithm and TOPSIS method
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DOI：10.1177/10567895261468060.png)
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Abstract
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<jats:p>Hybrid fiber-reinforced composite laminates are extensively employed across various engineering applications owing to their superior comprehensive mechanical performance. However, effectively balancing competing performance parameters remains critical to maximizing their overall structural capabilities and meeting practical engineering requirements. To address this challenge, this study presents a multi-objective optimization framework for the design of carbon/Kevlar fiber-reinforced composite laminates, with a focus on the influence of ply material and stacking sequence on tensile behavior under load. Initially, optimal Latin hypercube sampling (LHS) was employed to generate training samples, and a corresponding simulation database was established via finite element analysis to construct a surrogate model. A novel optimization framework was then developed by integrating a Kriging surrogate model with a hybrid strength Pareto evolutionary algorithm 2–multi-objective particle swarm optimization (SPEA2-MOPSO) algorithm and the technique for order preference by similarity to ideal solution (TOPSIS). Within this framework, the ply sequence and material type served as decision variables, while tensile strength, tensile modulus, and elongation at break were considered as optimization objectives. The research results show that the finite element simulation using the progressive damage model was verified by the tensile test, which confirmed the accuracy of damage prediction and revealed the synergistic effect among tensile performance parameters. A comparative analysis of three optimization algorithms, namely MOPSO, SPEA2, and the hybrid SPEA2-MOPSO, demonstrated the superior accuracy and efficiency of the hybrid approach in solving complex multi-objective problems. Finally, the application of the TOPSIS method enabled the identification of optimal trade-off solutions tailored to various engineering priorities, providing practical guidance for structural design decisions.</jats:p>
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