Abstract
A critical challenge in virtual fabric development is the lack of sample-free methods to accurately determine physical particle system parameters of flexible textiles, which currently rely on costly trial-and-error prototyping. This limitation stems from the inherent difficulties in reconstructing and controlling deformable body models, where real-time reconstruction fidelity and model accuracy are paramount. While progress has been made in computational efficiency, achieving high precision without physical samples remains unresolved, especially in the fabric design and manufacturing phase. This study proposes a data-driven surrogate modeling framework that directly links structural design parameters to system model parameters, enabling virtual collaborative design optimization. First, we define fabric morphomechanical parameters that are tailored to match physical particle systems. Next, in the absence of physical prototypes, we leverage structural design parameters to construct a yarn-level finite-element (FE) model, employing a decoupled simulation strategy to derive morphomechanical properties, which are then equivalently mapped to system parameters. Finally, using weft-plain fabric development as a case study, we train a machine-learning (ML)-based surrogate model to accelerate FE simulations while preserving >91.45% prediction accuracy. This work bridges the critical gap between design parameters and physical particle systems, offering a digital and visual solution for smart fabric optimization. The framework is scalable to advanced functional textiles (e.g., strain-sensing fabrics and piezoelectric nanofiber assemblies), significantly reducing R&D cycles and prototyping costs. By integrating multiscale simulation, data-driven modeling, and intelligent manufacturing, this research advances the computational design paradigm for next-generation fiber-based materials.
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