Key points are not available for this paper at this time.
Three-dimensional (3D) textile-based garment prototyping, widely adopted in the apparel and textile industry, enhances cost efficiency, work productivity, and seamless communication via visual prototyping. Neural network-based 3D textile digitization has the potential to streamline manufacturing processes by negating the need for traditional physical property (PT) measurements. However, a research gap exists concerning the accuracy of the technology and its applicability to advanced functional apparel manufacturing. The primary research question is to investigate how variations in digitized physical properties obtained from PT measurements and artificial intelligence (AI)-based textile digitization impact the accuracy of a fabric’s mechanical representation. In this study, we aimed to evaluate AI-based textile digitization accuracy using a drape test method. The drape coefficient (DC) analysis revealed that the PT-based simulated DC exhibited a normalized mean absolute error (NMAE) ranging from 2% to 11%, while the AI-based simulated DC showed a range of 3–51%. Notably, for the samples, except those with very limp or very stiff fabric samples, the AI-based simulation exhibited a NMAE within 3–15%.
Youn et al. (Mon,) studied this question.