Computational Investigation of Variable-Sensitivity Force Sensors: A Comparison of Polyurethane-based Shape-Memory Polymers

Shape-memory-polymer (SMP)-based force sensors (FS) enable adaptive sensitivity through thermally induced stiffness modulation, offering significant advantages for caregiving robotics. We previously developed and theoretically validated the experimental data for a novel thermo-responsive polyurethane (PUR) SMP-based FS with variable sensitivity and measuring range by combining concepts of stiffness change, structural modification, and cross-sectional shapes programming. In this study, we have proposed a computational framework based on finite element analysis (FEA) to compare the heat-triggered sensitivity-switching behavior of SMP-FS, providing a systematic understanding of how the selection of SMP materials affects FS performance. Our analysis is focused on four distinct PUR-based SMPs, each differing in stiffness and glass transition temperature. FEA is conducted using ANSYS, employing linear elastic models for the glassy state and viscoelastic constitutive models based on Prony series for the rubbery state. Force–displacement and force–strain responses are analyzed to quantify key performance metrics. The results reveal strong correlations between SMP modulus contrast, viscoelastic relaxations, and achievable sensitivity tuning range. In turn, this comparative framework demonstrates that the dynamic range of variable sensitivity FS can be effectively tailored solely through strategic material selection, without altering the sensor geometry.