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1. Designing Ferromagnetic Soft Robots (FerroSoRo) with Level-Set-Based Multiphysics Topology Optimization

   https://doi.org/10.1109/ICRA40945.2020.9197457  

   Tian, Jiawei; Zhao, Xuanhe; Gu, Xianfeng David; Chen, Shikui (September 2020, IEEE International Conference on Robotics and Automation (ICRA))

   null (Ed.)

   Soft active materials can generate flexible locomotion and change configurations through large deformations when subjected to an external environmental stimulus. They can be engineered to design 'soft machines' such as soft robots, compliant actuators, flexible electronics, or bionic medical devices. By embedding ferromagnetic particles into soft elastomer matrix, the ferromagnetic soft matter can generate flexible movement and shift morphology in response to the external magnetic field. By taking advantage of this physical property, soft active structures undergoing desired motions can be generated by tailoring the layouts of the ferromagnetic soft elastomers. Structural topology optimization has emerged as an attractive tool to achieve innovative structures by optimizing the material layout within a design domain, and it can be utilized to architect ferromagnetic soft active structures. In this paper, the level-set-based topology optimization method is employed to design ferromagnetic soft robots (FerroSoRo). The objective function comprises a sub-objective function for the kinematics requirement and a sub-objective function for minimum compliance. Shape sensitivity analysis is derived using the material time derivative and adjoint variable method. Three examples, including a gripper, an actuator, and a flytrap structure, are studied to demonstrate the effectiveness of the proposed framework. 

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2. Analytical modeling of a magnetoactive elastomer unimorph

   https://doi.org/10.1088/1361-665X/acece7  

   Pan, Tan; Leng, Rui; Uitz, Oliver; Seepersad, Carolyn; Ounaies, Zoubeida; Frecker, Mary (August 2023, Smart Materials and Structures)

   Abstract Magnetoactive elastomers (MAEs) are capable of large deformation, shape programming, and moderately large actuation forces when driven by an external magnetic field. These capabilities enable applications such as soft grippers, biomedical devices, and actuators. To facilitate complex shape deformation and enhanced range of motion, a unimorph can be designed with varying geometries, behave spatially varying multi-material properties, and be actuated with a non-uniform external magnetic field. To predict actuation performance under these complex conditions, an analytical model of a segmented MAE unimorph is developed based on beam theory with large deformation. The effect of the spatially-varying magnetic field is approximated using a segment-wise effective torque. The model accommodates spatially varying concentrations of magnetic particles and differentiates between the actuation mechanisms of hard and soft magnetic particles by accommodating different assumptions concerning the magnitude and direction of induced magnetization under a magnetic field. To validate the accuracy of the model predictions, four case studies are considered with various magnetic particles and matrix materials. Actuation performance is measured experimentally to validate the model for the case studies. The results show good agreement between experimental measurements and model predictions. A further parametric study is conducted to investigate the effects of the magnetic properties of particles and external magnetic fields on the free deflection. In addition, complex shape programming of the unimorph actuator is demonstrated by locally altering the geometric and material properties. 

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3. Fully 3D‐Printed Miniature Soft Hydraulic Actuators with Shape Memory Effect for Morphing and Manipulation

   https://doi.org/10.1002/adma.202402517  

   Qing, Haitao; Chi, Yinding; Hong, Yaoye; Zhao, Yao; Qi, Fangjie; Li, Yanbin; Yin, Jie (June 2024, Advanced Materials)

   Abstract Miniature shape‐morphing soft actuators driven by external stimuli and fluidic pressure hold great promise in morphing matter and small‐scale soft robotics. However, it remains challenging to achieve both rich shape morphing and shape locking in a fast and controlled way due to the limitations of actuation reversibility and fabrication. Here, fully 3D‐printed, sub‐millimeter thin‐plate‐like miniature soft hydraulic actuators with shape memory effect (SME) for programable fast shape morphing and shape locking, are reported. It combines commercial high‐resolution multi‐material 3D printing of stiff shape memory polymers (SMPs) and soft elastomers and direct printing of microfluidic channels and 2D/3D channel networks embedded in elastomers in a single print run. Leveraging spatial patterning of hybrid compositions and expansion heterogeneity of microfluidic channel networks for versatile hydraulically actuated shape morphing, including circular, wavy, helical, saddle, and warping shapes with various curvatures, are demonstrated. The morphed shapes can be temporarily locked and recover to their original planar forms repeatedly by activating SME of the SMPs. Utilizing the fast shape morphing and locking in the miniature actuators, their potential applications in non‐invasive manipulation of small‐scale objects and fragile living organisms, multimodal entanglement grasping, and energy‐saving manipulators, are demonstrated. 

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4. Encoding reprogrammable properties into magneto-mechanical materials via topology optimization

   https://doi.org/10.1038/s41524-023-00980-2  

   Zhao, Zhi; Zhang, Xiaojia Shelly (December 2023, npj Computational Materials)

   Abstract The properties of materials and structures typically remain fixed after being designed and manufactured. There is a growing interest in systems with the capability of altering their behaviors without changing geometries or material constitutions, because such reprogrammable behaviors could unlock multiple functionalities within a single design. We introduce an optimization-driven approach, based on multi-objective magneto-mechanical topology optimization, to design magneto-active metamaterials and structures whose properties can be seamlessly reprogrammed by switching on and off the external stimuli fields. This optimized material system exhibits one response under pure mechanical loading, and switches to a distinct response under joint mechanical and magnetic stimuli. We discover and experimentally demonstrate magneto-mechanical metamaterials and metastructures that realize a wide range of reprogrammable responses, including multi-functional actuation responses, adaptable snap-buckling behaviors, switchable deformation modes, and tunable bistability. The proposed approach paves the way for promising applications such as magnetic actuators, soft robots, and energy harvesters. 

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5. Design and optimization of magneto-active elastomer (MAE) unimorphs using genetic algorithms

   https://doi.org/10.1088/1361-665X/ae0f67  

   Pan, Tan; Greenwood, Taylor; Seepersad, Carolyn; Ounaies, Zoubeida; Frecker, Mary (October 2025, Smart Materials and Structures)

   Abstract Magneto-active elastomers (MAEs), a type of smart material, deform in response to external magnetic fields owing to their flexible, untethered, and reconfigurable capabilities. These capabilities are useful for biomedical devices, robotic systems, and actuators for which control over shape morphing, extent of deformation, and force output during actuation are desirable. However, programming the MAE to realize targeted functionalities such as complex deformations (e.g., biomimetic motions), or manipulating force (e.g., grippers) is challenging due to inherent trade-offs between deformation and force generation, where greater flexibility enables larger displacements but typically reduces force, necessitating a formal design and optimization approach. This work introduces a systematic design and optimization approach to program MAEs to achieve complex shape morphing and predictable force-displacement responses. The proposed approach enables optimization for target objectives by integrating previously developed analytical models for predicting the MAE actuation performance into evolutionary algorithms, including genetic algorithms (GAs) and the multi-objective non-dominated sorting (NSGA-II). The optimization output includes the geometry and magnetic properties of the MAE to achieve target deformations or forces as closely as possible when actuated by contactless magnetic fields. Specifically, the approach is used to minimize deviations between the actuated MAE and a predefined target shape and to examine the performance trade-offs between free deflection and blocked force. Two case studies are conducted to match the actuated MAE with predefined target shapes inspired by a gripper and a snake. Both studies demonstrate strong qualitative and quantitative agreements, with shape errors of 3 × 10⁻⁵cm and 8.9 × 10⁻⁴cm, respectively. The trade-offs between free deflection and blocked force are analyzed and illustrated using a Pareto front plot, which highlights the set of potential solutions. This research offers a promising avenue for revolutionizing traditional static structures by incorporating smart materials capable of altering their shapes with customized and predictable displacement and actuation force profiles. 

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