2019 - 2022

Estimation of yarn-level simulation models for production fabrics

| Research Collaborated with Industry Partners  | Published at SIGGRAPH 2022

| Under Armour, in collaboration with IST Austria, URJC Spain, SEDDI

I - Learning of yarn properties

  • Acquired undyed yarns in various parameters.

  • Developed and performed mechanical testing for yarns, which was rarely seen.

  • Measured multiple attributes with microscope.

II- Yarn to Fabrics

  • Learned fundamentals of flat and circular knitting machines

  • Experimented with different machine and yarn structure composition settings to influence textile properties

  • Built the basic assumptions of the project based off these early learnings

  • Practiced industry knitting at volume

III - Define and conduct a suite of mechanical testings

ACHEIVEMENTS

  • We present the first work demonstrating that yarn-level simulation is capable of replicating 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, which spans several complex knit patterns, yarn compositions, and yarn coatings, resulting in diverse physical properties like stiffness, nonlinearity, and anisotropy.

  • We developed a system for optimizing yarn-level parameters in order to match these real-world data, and we offer a few novel extensions to make yarn-level simulation models more capable of replicating the bi-phasic stiffness behavior of plated yarns and realworld contact scenarios.

  • Published and presented at SIGGRAPH 2022: https://dl.acm.org/doi/10.1145/3528223.3530167