2020/01/24 by Ziliang Kang, Kai A. James · 12 citations
Engineering · Materials Science · Mathematics · #Algorithm #Artificial intelligence #Benchmark (surveying) #Composite Structure Analysis and Optimization #Composite material #Computer science #Engineering #Finite element method #Martensite #Materials science #Mathematical optimization #Mathematics #Morphing #Parameterized complexity #Pseudoelasticity #SMA* #Shape Memory Alloy Transformations #Shape-memory alloy #Structural engineering #Topology (electrical circuits) #Topology Optimization in Engineering #Topology optimization #Transient (computer programming)
paper · doi:10.1002/nme.6319
published in International Journal for Numerical Methods in Engineering 121(11), 2558-2580 (Wiley)
openalex publication_date 2020/01/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/26
Summary We present a novel method for computational design of adaptive shape‐memory alloy (SMA) structures via topology optimization. By optimally distributing a SMA within the prescribed design domain, the proposed algorithm seeks to tailor the two‐way shape‐memory effect (TWSME) and pseudoelasticity response of the SMA materials. Using a phenomenological material model, the thermomechanical response of the SMA structure is solved through inelastic finite element analysis, while assuming a transient but spatially uniform temperature distribution. The material distribution is parameterized via a SIMP formulation, with gradient‐based optimization used to perform the optimization search. We derive a transient, bilevel adjoint formulation for analytically computing the design sensitivities. We demonstrate the proposed design framework using a series of two‐dimensional thermomechanical benchmark problems. These examples include design for optimal displacement due to the TWSME, and design for maximum mechanical advantage while accounting for pseudoelasticity.