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1. Energy and Force Optimization of a Network of Novel Electromagnetic Soft Actuators

   https://doi.org/10.3390/en13143572  

   Ebrahimi, Nafiseh; Jafari, Amir (July 2020, Energies)

   null (Ed.)

   This paper discusses how to optimally design polygonal profiles of Electromagnetic Soft Actuators (ESAs) to be used in a network to achieve maximum output force with minimum energy consumption. The soft actuators work based on operating principle of solenoids but are made of intrinsically soft materials. It was, previously, confirmed that by miniaturizing the size, the amount of output force decreases for a single ESA however, by the ratio of force to volume increases. Therefore, networking small sized ESAs, would increase the output force. Initially, ESAs were made with circular cross-section profiles. However, we prove here that the shape of the cross-section profile can affect the output force. A polygonal shape with fewer sides would result in higher output force for a single ESA. However, with a network of ESAs, another parameter, packing density, plays an important role in the output force. Our optimization results suggest that even though triangular cross-section profiles lead to the highest amount of force for a single ESA, the best choice would be hexagonal shapes when they are networked. 

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2. 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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3. 3D‐Printed Silicone Soft Architectures with Programmed Magneto‐Capillary Reconfiguration

   https://doi.org/10.1002/admt.201800528  

   Roh, Sangchul; Okello, Lilian_B; Golbasi, Nuran; Hankwitz, Jameson_P; Liu, Jessica_A‐C; Tracy, Joseph_B; Velev, Orlin_D (January 2019, Advanced Materials Technologies)

   Abstract Soft intelligent structures that are programmed to reshape and reconfigure under magnetic field can find applications such as in soft robotics and biomedical devices. Here, a new class of smart elastomeric architectures that undergo complex reconfiguration and shape change in applied magnetic fields, while floating on the surface of water, is reported. These magnetoactive soft actuators are fabricated by 3D printing with homocomposite silicone capillary ink. The ultrasoft actuators easily deform by the magnetic force exerted on carbonyl iron particles embedded in the silicone, as well as lateral capillary forces. The tensile and compressive moduli of the actuators are easily determined by their topological design through 3D printing. As a result, their responses can be engineered by the interplay of the intensity of the magnetic field gradient and the programmable moduli. 3D printing allows us to fabricate soft architectures with different actuation modes, such as isotropic/anisotropic contraction and multiple shape changes, as well as functional reconfiguration. Meshes that reconfigure in magnetic fields and respond to external stimuli by reshaping could serve as active tissue scaffolds for cell cultures and soft robots mimicking creatures that live on the surface of water. 

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4. Multi-material Topology Optimization of Ferromagnetic Soft Robots Using Reconciled Level Set Method

   Tian, Jiawei; Gu, Xianfeng David; Chen, Shikui (August 2021, Proceedings of the International Design Engineering Technical Conferences & Computers and Information in Engineering Conference)

   null (Ed.)

   Ferromagnetic soft materials can generate flexible mobility and changeable configurations under an external magnetic field. They are used in a wide variety of applications, such as soft robots, compliant actuators, flexible electronics, and bionic medical devices. The magnetic field enables fast and biologically safe remote control of the ferromagnetic soft material. The shape changes of ferromagnetic soft elastomers are driven by the ferromagnetic particles embedded in the matrix of a soft elastomer. The external magnetic field induces a magnetic torque on the magnetized soft material, causing it to deform. To achieve the desired motion, the soft active structure can be designed by tailoring the layouts of the ferromagnetic soft elastomers. This paper aims to optimize multi-material ferromagnetic actuators. Multi-material ferromagnetic flexible actuators are optimized for the desired kinematic performance using the reconciled level set method. This type of magnetically driven actuator can carry out more complex shape transformations by introducing ferromagnetic soft materials with more than one magnetization direction. Whereas many soft active actuators exist in the form of thin shells, the newly proposed extended level set method (X-LSM) is employed to perform conformal topology optimization of ferromagnetic soft actuators on the manifolds. The objective function comprises two sub-objective functions, one for the kinematic requirement and the other for minimal compliance. Shape sensitivity analysis is derived using the material time derivative and the adjoint variable method. Three examples are provided to demonstrate the effectiveness of the proposed framework. 

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5. INDEEDopt: a deep learning-based ReaxFF parameterization framework

   https://doi.org/10.1038/s41524-021-00534-4  

   Sengul, Mert Y.; Song, Yao; Nayir, Nadire; Gao, Yawei; Hung, Ying; Dasgupta, Tirthankar; van Duin, Adri C. T. (May 2021, npj Computational Materials)

   Abstract Empirical interatomic potentials require optimization of force field parameters to tune interatomic interactions to mimic ones obtained by quantum chemistry-based methods. The optimization of the parameters is complex and requires the development of new techniques. Here, we propose an INitial-DEsign Enhanced Deep learning-based OPTimization (INDEEDopt) framework to accelerate and improve the quality of the ReaxFF parameterization. The procedure starts with a Latin Hypercube Design (LHD) algorithm that is used to explore the parameter landscape extensively. The LHD passes the information about explored regions to a deep learning model, which finds the minimum discrepancy regions and eliminates unfeasible regions, and constructs a more comprehensive understanding of physically meaningful parameter space. We demonstrate the procedure here for the parameterization of a nickel–chromium binary force field and a tungsten–sulfide–carbon–oxygen–hydrogen quinary force field. We show that INDEEDopt produces improved accuracies in shorter development time compared to the conventional optimization method. 

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