This PDF file contains the front matter associated with SPIE Proceedings Volume 12484, including the Title Page, Copyright information, Table of Contents and Conference Committee lists.

Recent developments in soft autonomous matter strive to invest means for material intelligence whereby environmental stimuli is processed through unconventional computing methods. Combinational logic operations are recently explored through mechanologic techniques and reconfigurable integrated circuits in compliant materials. In this research we further advance information processing by introducing sequential logic in soft, and conductive mechanical materials with electroactive components. We develop a multilayer material platform to integrate electromechanical combinational logic layers with memory storage layers. Such non-volatile memory is introduced through structural multistability to obtain stable conductive network configurations. By employing liquid crystal elastomer components with joule heating elements in the material system, the memory bit layers are capable of self-control through the integrated circuit. We establish a mathematical method to program the mechanical computing platform and the conductive network based on Boolean characteristic functions. This design technique allows for the development of fundamental sequential logic operations such as flip flops, and counters. We also advance on such computing capabilities to demonstrate the basics of artificial intelligence through feedback processes. This research provides material systems composed of polymers with electroactive, conductive and multistable properties that can memorize and think about applied mechanical stress. Such findings in the fundamental multiphysics of information processing inspire a new class of soft autonomous matter with advanced computing capabilities.

Recent advancements in mechanical computing have facilitated the development of intelligent matter capable of sensing, processing, and adapting to environmental stimuli. Using mechanically abstracted bits, mechanological systems can perform digital logic operations based on the physical configuration of multimodal materials. Yet, many embodiments of mechanical logic are limited by the need to manually operate material systems to enter a desired configuration. Here, a framework is presented to design multistable material systems that can enter a programmable sequence of digital states through monotonically increasing shear input. By taking advantage of interactions between serial bistable mechanisms, mechanical bits can be deterministically activated and reset through simple displacement-controlled loading. The bistable units used in this work take advantage of two discrete self-contact regions that allow for highly tunable activation and snap-through behaviors. Using the mathematical model for a single unit, the principle of minimum total potential energy can be employed to determine the behavior of the multistability of a material system with bistable units in series.

The synthesis of stimuli-responsive materials and digital logic operations provides the opportunity to incorporate decision making in soft material systems. Liquid crystal elastomers (LCEs) are attractive materials for such capabilities by virtue of their adaptability and quick actuation to facilitate instantaneous and autonomous decision-making. LCEs are innately thermally responsive, though interesting functionalities of LCEs can be realized by controlling molecular order in the elastomeric network and introducing additives for response to non-thermal stimuli. Herein, carbon black-embedded LCE films with liquid metal-based conductive traces are designed to undergo logic operations based on ultraviolet (UV) barcode inputs. Buffer and NOT switches are laser cut in LCE films with twist-nematic molecular order to enable contact and release of conductive traces. Opposite switching behavior is achieved with selective polymerization of the LCE film in nematic and near-isotropic states. These two types of switches are connected in series and parallel to create basic logic gates that can then be combined into more complex logic operations. UV barcode inputs allow for localized photothermal response resulting from the carbon black particulates absorbing light and emitting heat, thus enabling logic gate computation through the design and assembly of switches. This reversible, repeatable actuation contributes to embedding decision-making capabilities in photo-responsive soft material systems

In this work, spherification was investigated as an incapsulation technique for an impact-responsive gel, with the ultimate objective of the final design being employed as protective equipment in the form of smart layers for protecting delicate goods in transit. The smart protective layers investigated utilised the controlled distribution of a polyborosiloxane based non-Newtonian polymer, namely shear stiffening gel (SSG), which can respond to an external stimulus i.e., a rapid mechanical load, by absorbing a large amount of energy, thus resulting in the protection of the aforementioned goods. At first instance, the constituents of the smart protective layers underwent mechanical characterisation, where the underlying mechanism of the SSG and its ability to absorb energy via means of a phase transition occurrence was established and quantified to be approximately five times higher compared to silicone. At a second stage, a thorough investigation of the optimal encapsulation method and geometrical arrangement was completed. The performance of the final design was assessed via static and dynamic tests which demonstrated that the layers containing SSG displayed superior performance compared to conventional ones, being able to autonomously offer protection to the substrates. In particular, the novel smart layers increased first and final compressive failure stresses by approximately 50%, whereas at the same time the maximum forces prior to failure in low velocity impact (LVI) tests were approximately 50% higher, across the investigated impact energy levels. The results of this work establish these novel smart protective layers as an ideal solution in a wide variety of applications where extremely fragile and valuable goods are in transit and impact forces need to be minimised or eliminated, such as camera lenses, electrical components, blood vials, and other medical products, overcoming the drawbacks of traditional packaging materials.

Molecular dynamics is a technique used to understand how the interactions between atoms control complex material behavior under internal and external forces. We use a game theoretical approach to simulate molecular interactions, build material surrogates, and study material nature as a function of the Coulomb potential. The electric Coulomb potential is applicable in studying electromagnetic properties, for instance, electrical conductivity and dielectric permittivity. We investigate how probability densities of material positions and charges influence the diffusion process induced by this potential. By exploiting the information hidden in the seemingly random molecular exchanges, we show that an information-theoretical measure of entropy can describe the dynamics of material interactions. In particular, we use the Renyi entropy to derive posterior density representing the most likely particle distribution after these exchanges and thus connect molecular dynamics to entropy dynamics.

Fractal structures are characterized by the self-similarity they exhibit at different length scales. This self-similarity can be characterized using a measure called the fractal dimension, which quantifies the complexity by the ratio of the change in geometry by the change in scale. This measure is used to describe these geometries mathematically by employing fractal order differential operators related to the structure’s fractal dimension. Fractal structures arise remarkably frequently in nature; some examples include snowflakes, lightning and blood vessels. In field-coupled intelligent materials, complex multi-scale material structures exhibit the same self-similar properties of fractals and therefore it is useful to model these structures in a fractal mathematical framework to better understand their behavior. Diffusion-limited aggregation (DLA) is an iterative process that produces a two-dimensional fractal structure, whereby particles undergo a random walk and cluster together to form larger structures of particles. The structures produced using this process can be observed in many natural systems such as electrodeposition and dielectric breakdown. A finite difference model was developed to simulate heat diffusion on different DLA structures with varying fractal dimension. The effect of the structure’s fractal dimension on the rate of heat transfer across the structure was analyzed.

Piezoelectric materials are excellent actuator candidates due to their high frequency bandwidth. However, hysteresis and nonlinear material effects can reduce their overall performance, particularly when driven at high amplitudes and high frequencies. Of interest here is an application for high-frequency actuation. The demand for actuation authority requires careful characterization and accurate modeling of the piezoelectric actuator dynamics to ensure the intended performance. This paper presents such a characterization of a ring-shaped piezoelectric stack actuator. A series of experiments is presented to explore the ring stack actuator’s response both under free boundary conditions and with spring-applied preloads. Fixed voltage tests conducted confirm the expected quasi-static response, while oscillatory tests exhibit dynamics that impact response at higher frequencies. Preload stiffnesses appear to minimally change the nominal displacement observed compared to the baseline, no-load case. While the preloads were small, the actuator showed qualitatively similar performance for the unloaded and several loaded cases.

Nanofiller-modified polymer matrix composites (PMCs) have been much studied as multi-functional materials. To date, however, work in this area has overwhelmingly focused on single-filler composites such as, for example, carbon nanotube (CNT)-modified polymers. Furthermore, work on multi-filler systems has focused on fillers with complementary attributes (e.g. achieving higher electrical conductivity in CNT + graphene composites than can be achieved via modification with either CNTs or graphene alone). As an alternative, we herein propose a new approach: dissimilar functional fillers selected for synergistic interactions. That is, we seek to identify combinations of nanofillers that interact with each other in order to give rise to unique multi-functional capabilities not achievable by either filler individually. To that end, we present preliminary work on CNT + carbon-coated iron nanoparticle (CCFeNP)-modified PMCs. CNTs and CCFeNPs were selected due to their complementary geometry (i.e. high aspect ratio CNTs and spherical CCFeNPs, which gives rise to higher electrical conductivity) and their potential for synergistic electro-magnetic interactions in a percolated network. These preliminary results report on the electro-magnetic properties of CNT + CCFeNP/epoxy composites including AC and DC conductivity and magnetic permeability as a function of varying CNT and CCFeNP concentration. It is hoped that further exploration into synergistic functional filler combinations will lead to new multi-functional capabilities in the future.

This study presents the use of a 3D printing method to create kerf structures that can be formed into complex geometries. Kerfing is a subtractive manufacturing method to create flexible surfaces out of stiff planar materials such as metal or wood sheets by removing portions of the materials. The kerf structures are characterized by the kerf pattern, such as square interlocked Archimedean spiral and hexagon spiral domain, cell size, and cut density. By controlling the kerf pattern, spatial density, cell size, and material, the local properties of the structure can be controlled and optimized to achieve the desired local flexibility while minimizing the stresses developed in the kerf structure. Since subtractive manufacturing limits the patterns and materials that can be considered in kerf structures, FDM 3D printing is explored to fabricate kerf structures using polymers, such as Polylactic acid (PLA) and Thermoplastic polyurethane (TPU), where it is possible to vary microstructural topology and materials within the kerf structures. 3D printing enables the combination of the two different polymers and tuning printing factors to create multifunctional kerf structures. The multifunctional kerf structures can then be actuated using non-mechanical stimulations, such as thermal, to shape them into complex geometries.

The key to monitoring structural health and defect detection of materials is the capacity to detect impact waves and their propagation through materials. A sensor must be extremely flexible and have a complex shape to detect impact waves from a certain type of construction. Complex sensors can be produced using direct ink write (DIW). In this article, the DIW approach is used to create a flexible impact wave propagation sensor (IWPS). Barium titanate (BaTiO3, or BTO), a ferroelectric ceramic material, is dispersed in polydimethylsiloxane (PDMS), which not only increases the flexibility of the 3D-printed sensor but also assures a consistent piezoelectric response across the entire sensor. This study investigated the impact load that caused an impact wave in a flexible sensor and its response to the impact load-generated impact wave. On BTO/PDMS stretchable composites, MWCNT (multi-walled carbon nanotube) based electrodes were printed using the DIW's multi-material printing capability. After contact poling of IWPS, 50wt% of BTO in the PDMS matrix produced a piezoelectric coefficient of 20 pC/N. Applying impact loading at the sensor's center caused an impact wave which eventually vanished as it got further away from the applied impact load's origin. The output voltage from several IWPS nodes was measured in order to characterize the propagation of impact waves. Additionally, the particle-wave velocity of a specific material attached to IWPS was calculated in this study using the voltage response time differences at various sensor locations. The particle-wave velocities of stainless steel (SS) and low-density polyethylene (LDPE) were measured using the specially built IWPS and were found to be 5625 m/s and 2000 m/s, respectively. These values are comparable with their theoretical values.

Recent developments have shown that spatial structures devised from origami or low-dimensional rigid linkage mechanisms can be used to construct deployable arrays for antennas or satellites. Yet, some of these structures are limited to deploying in fixed planes or directions. This research introduces a reconfigurable single-degree-of-freedom spatial structure devised from a Kresling-inspired mechanism and scissor arms. Analytical models are constructed to demonstrate compaction, deployment, and acoustic wave guiding capabilities of the proposed structure. A case study using the linear scissor arm configuration is presented to illustrate the modular scissor arm behavior and large deployment-to-compaction volume ratio of the system. A second case study is also presented to demonstrate the acoustic wave guiding capabilities of the Kresling-Scissor structure by utilizing spiral scissor arms, thereby proposing a novel concept for constructing deployable spiral wave guiding arrays. The results encourage broader exploration of the interfaces between origami structures and rigid linkage mechanisms.

In high-rise building structures, only using structural stiffness to resist the seismic energy is not economic and effective. Therefore, various energy dissipation devices are deployed to the structure, such as friction type energy dissipation device, Buckling Restrained Bracing (BRB) and viscous damper. Many researchers have been working on improving the performance of the dissipation devices. Though the plastic or residual deformation after earthquakes can consume the energy, the irreversible damage was introduced. In addition, its capability highly depends on the materials. Therefore, we proposes to take advantage of the mechanical bistablity to design a novel energy dissipation device, Mechanical bistability is defined as availability of two stable equilibrium configurations in the structure in response to the same loading conditions. The bistablity was realize by constructing a mechanical metamaterial: the snapping and buckling behavior were used to control the multistable response. The load-displacement curve was obtained by the analytical model. The results show that bistable stage was achieved. With bistablity and hysteretic characteristics, the proposed design can dissipate considerable energy. It provides a new strategy to develop the energy dissipation device.

In this study, a magneto-active elastomer (MAE) unimorph actuator is optimally designed for shape programming. An optimization design approach for the MAE unimorph actuator is developed to design the geometry and material properties of the structure. Within the design optimization, a previously developed and validated analytical model is applied to predict the actuation performance of MAE unimorph actuator under a user-specified external magnetic field. This model considers the unimorph as a segmented beam with large deflections and approximates the response of the material to the magnetic field as segment-wise applied torques. Then a single objective function representing the shape error is minimized using a genetic algorithm (GA). The multi-objective non-dominated sorting genetic algorithm II (NSGA-II) is also implemented to maximize both normalized free deflection and blocked force. A Pareto set of optimal solutions is obtained and the best design can be selected based on application requirements. This work has the potential to provide tailorable shape change in medical device applications that require adjustments over time such as changing patient anatomy as treatment progresses. The devices can be actuated under an external magnetic field without wires or human interference. Additive manufacturing can realize the feasibility of fabrication for the designed MAE unimorph actuator by printing complex geometry and spatially tailored magnetic and mechanical properties.

This paper presents mechanics based tools for the design of multifunctional polymer composite materials. The class of composite discussed consist of a low permittivity matrix material (typically polymer) with ferroelectric inclusions (piezoelectric/ferroelectric particles, rods, platelets) dispersed throughout. The high permittivity of the inclusions causes the electric field to concentrate in the matrix, which makes it challenging to get the electric field into the inclusions. Elasticity (biharmonic Laplace’s equation) and electrostatics (harmonic Laplace’s equation), provide closed form solutions for single inclusion geometries, providing the electric field distribution in the inclusion and in the matrix. These solutions are used to validate finite element models used to address interactions between inclusions. This discussion addresses 2-D dielectric circular inclusions embedded in a linear polymer matrix. The approach is readily extended to other inclusion geometries such as ellipse, sphere, and ellipsoid (plates and rods).

Soft body armors, utilized to mitigate impact damage for the users, are typically composed of layered composites of high strength and toughness woven Kevlar fabrics. Recently, extensive attempts have been made to improve the impact mitigation performance of Kevlar body armor through the incorporation of stimulus-responsive smart materials. For instance, shear thickening or dilatant compounds have been used due to their viscosity changes in response to loading rate. One of the common shear thickening materials is polyborodimethylsiloxane (PBDMS). PBDMS polymer chains possess dynamic cross-links that can break and reform depending on the loading rate, transforming the material from a highly viscous gel to a rigid solid. In addition, nanomaterials have been demonstrated to be capable of effectively enhancing the mechanical properties of polymers and enabling sensing mechanical damages based on strain measurements. Therefore, to further enhance the impact protection performance of body armors, this work aims to develop a responsive and flexible composite with force sensing capabilities by incorporating CNTs and PBDMS with Kevlar fabrics. In particular, additive manufacturing procedures for fabricating the composite material system were developed. A series of mechanical experiments, including quasi-static and dynamic tests, have been conducted to characterize the mechanical performance of the PBDMS-coated Kevlar. In addition, the sensing performance of the CNT/PBDMS-Kevlar composites was evaluated via electromechanical tests. Overall, the PBDMS material system synthesized in this project exhibited dynamic crosslink-based shear stiffening behavior and can effectively reinforce the mechanical performance of Kevlar fabrics, especially under dynamic impact loads. In addition, CNTs and PBDMS were uniformly incorporated with the Kevlar fabrics, which demonstrated a promising approach to detect potential damage on soft body armors.

The rise of environmental concerns and the worldwide transition to a circular economy is partly fueling the discovery of new materials with unique functionalities that satisfy industry requirements. Polymer composites are among the most popular materials in various metal-replacement applications, such as auto and aerospace light-weighting, electronics, optics and energy storage devices. Nonetheless, their vast compositional design freedom and sensitivity to ambient conditions require extensive physical experiments. To address this issue, the aim of this research is to develop machine learning (ML) algorithms which are able to characterize entire stress-strain curves of polymer composites based on their composition, processing and environmental conditions. Three distinct feature variables including temperature, filler content and strain were utilized to predict the output stress amounts. The results indicated that the artificial neural network (ANN) models, with a new train-test splitting strategy, accurately fit the training data, as evidenced by the root mean squared error (RMSE) values below 3 MPa for PET composites and below 1 MPa for PC composites. Furthermore, the developed ANN models revealed a satisfactory performance on the testing data, with RMSE of less than 1.5 MPa for PC composites and approximately 6 MPa for PET composites. These findings pointed out the effectiveness of ANN models in predicting complete stress-strain curves of polymer composites, especially when a sufficient amount of data is available. The outcomes obtained will pave the way for automated design and characterization of advanced multifunctional composites while minimizing extensive physical testing, thereby advancing the vision set out by Industry 4.0.

Polymer nanocomposites typically possess heterogeneous microstructures that significantly affect structure-property relationships of these material systems. Various microscopic imaging techniques, such as optical microscopy, scanning electron microscopy (SEM), and X-ray microscopy, are essential for characterizing nanocomposite material systems and have provided informative insights of microstructural features. However, microscopic imaging through experiments can be expensive when large amounts of microstructural data are needed. One promising approach to address the imaging limitation and more efficiently generate large microstructural dataset is to statistically reconstruct similar images from a single original input image. A common method used to generate statistically equivalent images is the simulated annealing optimization algorithm. However, due to the high computational cost associated with the stochastic search path used in the simulated annealing algorithm, it can be challenging to reconstruct images with a high degree of agreement. Thus, in this study, a novel and more efficient image reconstruction method was developed by optimizing the simulated annealing algorithm through the manipulation of search path domain and available statistical information. The optimization technique was implemented to reconstruct several example two-dimensional (2D) images to evaluate its capabilities.