The rapid development of intelligent manufacturing and digitalization has promoted the wider application of digital weaving technology in the textile industry, while the simultaneous optimization of functional performance and aesthetic quality in textile structures remains a major challenge. This study aims to address this issue by integrating multi objective optimization algorithms with digital weaving processes to develop a design method that enhances both mechanical and aesthetic properties of fabrics. Using polyester and cotton blended yarns as the research materials, the study applies parameterized digital weaving, genetic algorithms, and particle swarm optimization to adjust key structural variables including yarn density, weaving angle, and fiber blending ratio. The fabrics produced under the optimized design are evaluated through standardized mechanical, comfort related, and aesthetic tests. The results show notable improvements in tensile strength and breathability, a reduction in bending stiffness that reflects enhanced flexibility, and measurable gains in color uniformity, texture finesse, and tactile comfort when compared with a traditionally woven control sample. These findings confirm that the coordinated adjustment of structural parameters, particularly the interaction between yarn density and a 45 degree twill weave, plays a central role in achieving a balanced enhancement of functionality and aesthetic performance. The study concludes that the proposed design framework based on digital weaving provides an effective approach for the development of high performance textiles with improved aesthetic appeal, supporting advanced manufacturing, personalized customization, and potential applications in areas such as sportswear and medical textiles.

digital weaving technology, functional textile structures, structural optimization, aesthetic performance, intelligent manufacturing