Litcius/Paper detail

Estimation of yarn-level simulation models for production fabrics

Georg Sperl, Rosa María Sánchez-Banderas, Manwen Li, Chris Wojtan, Miguel Á. Otaduy

2022ACM Transactions on Graphics40 citationsDOI

Abstract

This paper introduces a methodology for inverse-modeling of yarn-level mechanics of cloth, based on the mechanical response of fabrics in the real world. We compiled a database from physical tests of several different knitted fabrics used in the textile industry. These data span different types of complex knit patterns, yarn compositions, and fabric finishes, and the results demonstrate diverse physical properties like stiffness, nonlinearity, and anisotropy. We then develop a system for approximating these mechanical responses with yarn-level cloth simulation. To do so, we introduce an efficient pipeline for converting between fabric-level data and yarn-level simulation, including a novel swatch-level approximation for speeding up computation, and some small-but-necessary extensions to yarn-level models used in computer graphics. The dataset used for this paper can be found at http://mslab.es/projects/YarnLevelFabrics.

Topics & Concepts

YarnComputer scienceComputationGraphicsStiffnessComputer graphicsPipeline (software)TextileComputational scienceAlgorithmComputer graphics (images)Structural engineeringEngineeringMechanical engineeringMaterials scienceComposite materialProgramming languageTextile materials and evaluations3D Shape Modeling and AnalysisComputer Graphics and Visualization Techniques