Auxetic Kirigami Metamaterials upon Large Stretching

Hybrid-Microstructure-Based Soft Network Materials with Independent Tunability of Mechanical Properties over Large Deformations

Introducing auxetic metamaterials into stretchable electronics shows promising prospects for enhancing the performance and innovating the functionalities of various devices, such as stretchable strain sensors. Nevertheless, most existing auxetics fail to meet the requirement of stretchable electronics, which typically include high mechanical flexibility and stable Poisson's ratio over large deformations. Moreover, despite being highly advantageous for application in diverse load-bearing conditions, achieving tunability of J-shaped stress-strain response independent of negative Poisson's ratio remains a significant challenge. This paper introduces a class of hybrid-microstructure-based soft network materials (HMSNMs) consisting of different types of microstructures along the loading and transverse directions. The J-shaped stress-strain curve and nonlinear Poisson's ratio for HMSNMs can be tuned independently of each other. The HMSNM provides much higher strength than the corresponding existing metamaterial while offering a nearly stable negative Poisson's ratio over large strains. Both mechanical properties under infinitesimal and large deformations can be well-tuned by geometric parameters. Fascinating functionalities such as shape programming and stress regulation are achieved by integrating a set of HMSNMs in series/parallel configurations. A stretchable LED-integrated display capable of displaying dynamic images without distortion under uniaxial stretching serves as a demonstrative application.

Inverse design of Bézier curve-based mechanical metamaterials with programmable negative thermal expansion and negative Poisson's ratio via a data augmented deep autoencoder

Controlling stress and deformation induced by thermo-mechanical stimulation in high-precision mechanical systems can be achieved by mechanical metamaterials (MM) exhibiting negative thermal expansion (NTE) and negative Poisson's ratio (NPR). However, the inverse design of MM exhibiting a wide range of arbitrary target NTEs and NPRs is a challenging task due to the low design flexibility of analytical methods and parametric studies based on numerical simulation. In this study, we propose Bézier curve-based programmable chiral mechanical metamaterials (BPCMs) and a deep autoencoder-based inverse design model (DAIM) for the inverse design of BPCMs. Through iterative transfer learning with data augmentation, DAIM can generate BPCMs with a curved rib shape inaccessible with the Bézier curve, which improves the inverse design performance of the DAIM in the data sparse domain. This approach decreases the mean absolute error of NTE and NPR between the inverse design target and the numerical simulation results of inverse designed BPCMs on the data-sparse domain by 79.25% and 83.33% on average, respectively. A 3D-printed BPCM is validated experimentally and exhibits good coincidence with the target NTE and NPR. Our proposed BPCM and the corresponding inverse design framework enable the inverse design of BPCMs with NTE in the range of -1100 to 0 ppm K-1 and NPR in the range of -0.6 to -0.1. Furthermore, programmable thermal deformation modes with a fixed Poisson's ratio are realized by combining various inverse designed BPCM unit cells. BPCMs and the DAIM for their inverse design are expected to improve the mechanical robustness of high-precision mechanical systems through tunable modulation of thermo-mechanical stimulation.

Physics-aware differentiable design of magnetically actuated kirigami for shape morphing

Shape morphing that transforms morphologies in response to stimuli is crucial for future multifunctional systems. While kirigami holds great promise in enhancing shape-morphing, existing designs primarily focus on kinematics and overlook the underlying physics. This study introduces a differentiable inverse design framework that considers the physical interplay between geometry, materials, and stimuli of active kirigami, made by soft material embedded with magnetic particles, to realize target shape-morphing upon magnetic excitation. We achieve this by combining differentiable kinematics and energy models into a constrained optimization, simultaneously designing the cuts and magnetization orientations to ensure kinematic and physical feasibility. Complex kirigami designs are obtained automatically with unparalleled efficiency, which can be remotely controlled to morph into intricate target shapes and even multiple states. The proposed framework can be extended to accommodate various active systems, bridging geometry and physics to push the frontiers in shape-morphing applications, like flexible electronics and minimally invasive surgery.