Shape memory structures

Stepwise shape recovery

形状记忆结构（Shape Memory Structures, SMS）是一类能够在外界刺激下恢复至预设形状的智能结构。与传统形状记忆聚合物（SMP）不同，SMS展现了极高的弹性、形状保持性和恢复性，并且其编程过程不需要加热，而是可以通过简单的机械变形来完成，这使得操作更加简便和高效。

SMS 的独特优势

全弹性： SMS 由高弹性材料构成，在变形和恢复过程中不会经历玻璃化转变（Tg）或熔化/结晶转变。这意味着材料的刚度不会随着温度变化大幅降低，结构在任何形状下都保持良好的力学强度，不会像一些传统形状记忆材料在加热时变得过于柔软，导致无法支撑自身重量。
精确的形状设计： SMS 的一个显著特点是其变形可以通过结构设计进行精确控制。通过对几何形状的设计与优化，SMS 可以实现复杂的、可预测的变形路径和形状。每个变形后的形状都是稳定的，并且可以通过进一步的结构设计，决定其形变恢复的顺序。
高形状保持性与恢复性： SMS 在形状固定性（Shape Fixity Ratio）和形状恢复性（Shape Recovery Ratio）上都能达到 100%。这意味着，无论是变形后维持某一形状的能力，还是恢复至原始形状的能力，SMS 都表现出优异的性能。材料在保持形状时不会因时间或外部环境的影响而松弛或变形，这使其在长期使用中具有极高的稳定性。
形变恢复顺序可控： SMS 的独特之处在于，通过合理的结构设计，可以控制变形后的不同部位在恢复过程中的顺序。这为应用中的精确操控提供了可能性，能够用于设计特定的功能，如依次展开或逐步释放的机械结构。
可控刚度变化： 虽然 SMS 材料本身具有一定的弹性刚度，但其刚度的变化不会像传统形状记忆材料那样依赖于玻璃化转变温度（Tg）或熔化/结晶转变过程。这使得结构在整个工作过程中保持较为一致的力学性能，避免了传统材料变软无法支撑负载的问题。

应用前景由于其独特的性能，形状记忆结构在航空航天、机器人技术、可穿戴设备和柔性电子等领域具有广泛的应用前景。例如，在航天器中，SMS 可以用来制造可展开的天线或太阳能电池阵列，这些组件能够在不依赖外部加热的情况下展开并锁定到位，确保结构强度和功能稳定性。此外，在医疗器械和柔性机器人领域，SMS 也可以用于制造复杂的变形机构，以满足柔性、轻质和精准操控的需求。
总之，形状记忆结构凭借其全弹性、简单编程、高强度和可控恢复特性，突破了传统形状记忆聚合物的局限性，展现出广阔的应用前景。

Shape Memory Structures (SMS) are a class of smart structures that can recover to a pre-programmed shape upon external stimuli. Unlike traditional Shape Memory Polymers (SMP), SMS exhibit full elasticity, excellent shape fixity, and recovery, with a much simpler programming process that does not require heating. The deformation is controlled through structural design, making the system efficient and predictable.

Unique Advantages of SMS

Full Elasticity: SMS are made from highly elastic materials that do not rely on phase transitions like glass transition (Tg) or melting/crystallization to achieve their shape memory effect. This means the stiffness of the structure remains stable, providing mechanical strength at all stages of deformation, unlike traditional SMPs that soften and lose the ability to support their own weight when heated.
Precisely Designed Deformation: One of the standout features of SMS is the ability to design and control deformation paths through careful structural design. Geometrical optimizations enable complex and predictable deformation sequences, where each intermediate shape is stable, and the order of shape recovery can be controlled based on the design.
High Shape Fixity and Recovery Ratios: SMS achieve 100% shape fixity and shape recovery ratios, meaning they can hold a deformed shape perfectly and recover completely to their original shape when triggered. This high performance ensures long-term stability, as the structures maintain their intended shapes without relaxation or deformation over time.
Controllable Shape Recovery Sequence: By designing the structural elements with different dimensions and stiffness properties, the recovery sequence can be precisely controlled. This enables functionalities like sequential deployment of components, making SMS particularly useful in applications requiring precise mechanical actions.
Maintained Stiffness and Strength: While SMS materials offer some flexibility in stiffness, their mechanical performance is not dependent on phase changes such as glass transition or melting. This ensures that the structure retains its mechanical integrity even when subjected to deformation, preventing the softening or collapse that may occur in traditional SMPs.

ApplicationsDue to their unique properties, Shape Memory Structures have a wide range of potential applications in fields like aerospace, robotics, wearable devices, and flexible electronics. For instance, in aerospace, SMS can be used to create deployable antennas or solar arrays that can expand and lock into place without relying on external heating, ensuring structural strength and stability. In medical devices and soft robotics, SMS could enable the creation of complex, flexible deformation mechanisms that allow for precise control and lightweight, adaptable designs.
In summary, Shape Memory Structures overcome many of the limitations of traditional shape memory polymers with their full elasticity, simple programming, high strength, and controllable recovery properties. This opens up exciting possibilities for their use in various high-tech applications.

Extension/contraction

Step-by-step recovery of 3D printed multiple stable structures

(MSc) Cui J
(FYP) Lim JY/Wong JC

Shearing

Stepwise shape recovery @room temperature

Mechano/heating-responsive shape memory structures

with Dr Wang TX (NAAU)

(FYP) Lim WY

(FYP) Lim JY, Wong JC

Step by step shape switch 20250330

(FYP) Wong JC

Step by step shape recovery in "Symmetric" structures 202503a

(MSc) Cui J

3D printed shape memory structure: heating for shape recovery

Slow recovery

(URECA) Brian Ye Htet Lin

Straw version

Cooling in fridge

W/WO pre-cooling

(P-FYP) Paing HK

3 min vs 5 min cross-linked samples

Magneto-responsive shape memory structures

Concept

Electro-responsive shape memory structures

In a similiar way

Metamaterials vs metastructures

In engineering and physics, metamaterials and metastructures represent a shift from relying on chemistry to relying on geometry and architecture to achieve unique physical properties.
The primary difference lies in the scale of operation and how the system interacts with external fields or forces.

Metamaterials (Material-Level Focus)
A metamaterial is an artificially engineered composite whose extraordinary properties are derived from its deliberately designed internal geometry (unit cells or "meta-atoms") rather than the chemical composition of its base substances.

Key Characteristic: The internal repeating patterns are typically subwavelength—meaning they are smaller than the wavelength of the energy wave (light, sound, stress waves) they interact with.
Behavior: Because the structures are so small, the waves perceive the composite as a single, continuous, homogeneous material with exotic properties absent in nature.
Examples: Materials with a negative Poisson’s ratio (expanding when stretched instead of thinning), acoustic cloaking surfaces, or negative refractive index optics.

Metastructures (Structure-Level Focus)
A metastructure is a higher-level, macro-scale engineering system built by intentionally assembling metamaterials, mechanism-based arrays, or compliant elements to achieve unusual, macro-scale structural performance.

Key Characteristic: Unlike metamaterials, a metastructure operates at a macroscopic, visible scale. Its performance is analyzed as an overall structural assembly or framework rather than being homogenized into an abstract "material property".
Behavior: They leverage structural mechanics (like buckling, snapping, multi-stability, or kinematic linkages) to achieve large-scale transformations or energy absorption.
Examples: An airplane wing made of origami-inspired panels that morph its shape mid-flight, deployable spacecraft antennas, or shock-absorbing automotive chassis segments built from multi-stable lattices.

Summary of Differences

Primary Scale
Analysis
Core Function

Metamaterials
Microscopic / Subwavelength unit cells
Treated as a continuous, uniform material
Exotic properties (e.g., negative stiffness)

Metastructures
Macroscopic / Structural system scale
Treated as an engineered device or assembly
Large-scale morphing, folding, or deployment

Inverse design of multistable kirigami metamaterial via geometry-enabled shape programming and transforming

几何可编程剪纸超材料逆向设计的多稳态形状重构与性能调控

The inverse design of metamaterials with desired properties represents a significant challenge in mechanical science. Despite the potential demonstrated by recent algorithm models, their adoption has been limited by constraints such as the geometric limitations of elementary building cells. The use of the kirigami principle, which offers large deformation and nonlinear stiffness, has been explored. However, existing kirigami geometries, which remain isotropic, may restrict the design space. Our objective is to leverage the capabilities of geometry in shape programming and transformation to provide a framework for inverse design. This framework utilizes a unified geometry in kirigami cutting that is easily parameterized to generate independent anisotropic deformation and bistability. By integrating machine learning with a genetic algorithm, we achieve an inverse design process. The resulting kirigami architectures can be preprogrammed into target shapes and transformed between multiple stable states. This work underscores the significance of cell geometry topology, offering a powerful tool for the inverse design of metamaterials with reconfigurable and tailored mechanical properties, applicable in various fields such as robotics, electronics, and beyond.