Data-Driven Pathway to Predict Stimuli-Responses of Hollow Polymers

Melt-extruded hollow thermoplastic polymers are a new class of materials for stimuli-responsive applications, i.e., shape reconfigurable robotic clothes, ultra-stretchable touch and twist sensors, and programmable knitted textiles. Under large deformation (stimuli), such materials exhibit hyperelasticity. However, the stimuli-responsive functional behavior of such materials remains a crucial knowledge gap that persists in literature due to time and strain of energy-dependent property variations. This talk will introduce a data-driven pathway harnessing integrated computational materials engineering (ICME) to study, predict, and optimize hyperelastic hollow polymer fibers’ behavior. To do so, we calibrated properties utilizing a constitutive materials model, extracted the parametric values using TN (three networks) model, and emulated experimental stress-strain behavior in a finite-element-based multiphysics environment with 99.99% confidence. We also tested and verified our model’s accuracy for different compositions of thermoplastics and fabrics. Optimizing the stimuli-responsive functional behavior from hollow thermoplastic fibers requires numerous cost-incurring melt-extrusion runs to characterize strain-dependent behavior. However, from our proposed finite element analysis, we can not only predict strain-dependent behavior but also change shape and geometries for new understandings without running additional melt-extrusions or experiments. Additionally, our ICME approach can be extended to other stimuli (e.g., temperature, humidity, and chemical exposure) for the next generation of pre-programmed functional device fabrication from hyperelastic hollow fibers that may not even exist to date (i.e., body-worn programmable digital clothes).