Thermomechanical Behavior of Kirigami-Based Shape-Memory PLA Structures: Finite Element Analysis and Experimental Investigation


Balcı H. Ş., Balcı F., Ekinci İ., Ali D.

POLYMERS, cilt.18, ss.1-32, 2026 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 18
  • Basım Tarihi: 2026
  • Dergi Adı: POLYMERS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED)
  • Sayfa Sayıları: ss.1-32
  • Gazi Üniversitesi Adresli: Evet

Özet

Deployable shape-memory polymer lattice architectures enable transitions between compact temporary configurations and recovered geometries, with their performance governed by cellular topology, compressibility, shape recovery, and post-cycle mechanical stability. This study numerically and experimentally investigated kirigami-based shape-memory PLA structures as an engineering proof of concept. The main novelty is the integration of three unscaled kirigami cores—Rotating Squares, Honeycomb, and Re-entrant—within a common deployable reference frame across 19 configurations. By comparing finite element predictions with experiments on FDM-printed PLA structures, the study identifies topology-dependent trade-offs among compressibility, shape fixity, shape recovery, and post-cycle mechanical performance, as well as limitations of numerical prediction. Hybrid structures exhibited greater total compressibility than the reference frame and corresponding cores; H-RE-80 showed the highest values, at 74.4% numerically and 76.8% experimentally. H-RS structures showed controlled deformation behavior but did not consistently pre-serve their initial stiffness after cycling; H-HC structures showed a comparatively balanced overall performance, and H-RE structures provided the greatest geometric reduction. Numerical predictions reproduced the main compression trends but showed geometry-dependent deviations in shape fixity and effective elastic modulus. Overall, architecture selection should balance compactability, shape-memory response, and post-recovery mechanical function rather than maximize a single metric.