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1. Review of Morphing Laminated Composites

   https://doi.org/10.1115/1.4044269  

   Chillara, V. S. ; Dapino, M. J. ( January 2020 , Applied Mechanics Reviews)

   Abstract Morphing structures, defined as body panels that are capable of a drastic autonomous shape transformation, have gained importance in the aerospace, automotive, and soft robotics industries since they address the need to switch between shapes for optimal performance over the range of operation. Laminated composites are attractive for morphing because multiple laminae, each serving a specific function, can be combined to address multiple functional requirements such as shape transformation, structural integrity, safety, aerodynamic performance, and minimal actuation energy. This paper presents a review of laminated composite designs for morphing structures. The trends in morphing composites research are outlined and the literature on laminated composites is categorized based on deformation modes and multifunctional approaches. Materials commonly used in morphing structures are classified based on their properties. Composite designs for various morphing modes such as stretching, flexure, and folding are summarized and their performance is compared. Based on the literature, the laminae in an n-layered composite are classified based on function into three types: constraining, adaptive, and prestressed. A general analytical modeling framework is presented for composites comprising the three types of functional laminae. Modeling developments for each morphing mode and for actuation using smart material-based active layers are discussed. Results, presented for each deformation mode, indicate that the analytical modeling can not only provide insight into the structure's mechanics but also serve as a guide for geometric design and material selection. 

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2. PRECISE 3D STRUCTURING OF POLYMER NANOCOMPOSITES USING TRIAXIAL MAGNETIC FIELDS

   Atescan, Yagmur ; Yamamoto, Namiko. ( January 2019 , Composites)

   Polymer nanocomposites have been sought after for their light weight, high performance (strength-to-mass ratio, renewability, etc.), and multi-functionality (actuation, sensing, protection against lightning strikes, etc.). Nano-/micro-engineering has achieved such advanced properties by controlling crystallinity, phases, and interfaces/interphases; hierarchical structuring, often bio-inspired, has been also implemented. While driven by the advanced properties of nanofillers, properties of polymer nanocomposites are critically affected by their structuring and interfaces/interphases due to their small size (< ~50 nm) and large surface area per volume. Measures of their property improvement by nanofiller addition are often smaller than theoretically predicted. Currently, application of these novel engineered materials is limited because these materials cannot often be made in large sizes without compromising nano-scale organization, and because their multi-scale structure-property relationships are not well understood. In this work, we study precise and fast nanofiller structuring with non-contact and energy-efficient application of oscillating magnetic fields. Magnetic assembly is a promising, scalable method to deliver bulk amount of nanocomposites while maintaining organized nanofiller structure throughout the composite volume. In the past, we have demonstrated controlled alignment of nanofillers with tunable inter-assembly distances with application of oscillating one-dimentional magnetic fields (~100s of G), by taking advantage of both magnetic attraction and repulsion. The low oscillation frequency (< 1 Hz) was tuned to achieve maghemite nanofiller alignment patterns, in an epoxy matrix, with different amount of inter-nanofiller contacts with the same nanofiller volume fraction (see Figure 1a). This work was recently expanded to three-dimensional assembly using a triaxial Helmholtz coil system (see Figure 1b); the system can apply the triaxial magnetic fields of varying magnitude (max. ±300G, ±250G, ±180G (x-y-z)) and frequency (0 to 1 Hz, ~0.1 Hz resolution) with controlled phase delay to the sample size of 1.5” x 2.5” x 3.5”(x-y-z). Two model systems are currently studied: maghemite nanofillers in an elastomer for magnetoactuation, and nickel-coated CNTs in an thermoset for mehcniacl and transport property reinforcement. The assembled nanofiller structures are currently characterized by microCT; microCT scan data (see Figure 1b) are segmented through a machine learning algorithm, and will be modeled for their transport properties using a Monte Carlo method. These estimated properties will be compared with the experimentally characterized mechanical, transport, and actuation properties, providing the structure-interphase-property relationships. In future, we plan to integrate these nanocomposites to CFRPs for interlaminar property reinforcement, possibly with an out-of-autoclave composite processing. 

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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)

   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. 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)

   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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5. Multi-Objective Optimization of Predicted Magnetic Properties From Multifield Processing Conditions in Polymer Matrix Particle Composites

   https://doi.org/10.1115/SMASIS2022-91175  

   Widdowson, Denise ; Erol, Anil ; Papula, Dashiell ; Ounaies, Zoubeida ; von Lockette, Paris ( September 2022 , Proceedings of the ASME 2022 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASME 2022 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. Dearborn, Michigan, USA. September 12–14, 2022. V001T02A008. ASME.)

   Additive manufacturing, no longer reserved exclusively for prototyping components, can create parts with complex geometries and locally tailored properties. For example, multiple homogenous material sources can be used in different regions of a print or be mixed during printing to define properties locally. Additionally, heterogeneous composites provide an opportunity for another level of tuning properties through processing. For example, within particulate-filled polymer matrix composites before curing, the presence of an applied electric and/or magnetic fields can reorient filler particles and form hierarchical structures depending on the fields applied. Control of particle organization is important because effective material properties are highly dependent on the distribution of filler material within composites once cured. While previous work in homogenization and effective medium theories have determined properties based upon ideal analytic distributions of particle orientations and spatial location, this work expands upon these methods generating discrete distributions from quasi-Monte Carlo simulations of the electromagnetic processing event. Results of simulations provide predicted microarchitectures from which effective properties are determined via computational homogenization.

   These particle dynamics simulations account for dielectric and magnetic forces and torques in addition to hydrodynamic forces and hard particle separation. As such, the distributions generated are processing field dependent. The effective properties for a composite represented by this distribution are determined via computational homogenization using finite element analysis (FEA). This provides a path from constituents, through processing parameters to effective material properties. In this work, we use these simulations in conjunction with a multi-objective optimization scheme to resolve the relationships between processing conditions and effective properties, to inform field-assisted additive manufacturing processes.

   The constituent set providing the largest range of properties can be found using optimization techniques applied to the aforementioned simulation framework. This key information provides a recipe for tailoring properties for additive manufacturing design and production. For example, our simulation results show that stiffness for a 10% filler volume fraction can increase by 34% when aligned by an electric field as compared to a randomly distributed composite. The stiffness of this aligned sample is also 29% higher in the direction of the alignment than perpendicular to it, which only differs by 5% from the random case [1]. Understanding this behavior and accurately predicting composite properties is key to producing field processed composites and prints. Material property predictions compare favorably to effective medium theory and experimentation with trends in elastic and magnetic effective properties demonstrating the same anisotropic behavior as a result of applied field processing. This work will address the high computational expense of physics simulation based objective functions by using efficient algorithms and data structures. We will present an optimization framework using nested gradient searches for micro barium hexaferrite particles in a PDMS matrix, optimizing on composite magnetization to determine the volume fraction of filler that will provide the largest range of properties by varying the applied electric and magnetic fields.

    

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