This PDF file contains the front matter associated with SPIE Proceedings Volume 11589, including the Title Page, Copyright information, and Table of Contents.

In the field of soft robotics, harnessing the nonlinear dynamics of soft and compliant bodies as a computational resource to enable embodied intelligence and control is known as morphological computation. Physical reservoir computing (PRC) is a true instance of morphological computation wherein; a physical nonlinear dynamic system is used as a fixed reservoir to perform complex computational tasks. These dynamic reservoirs can be used to approximate nonlinear dynamical systems and even perform machine learning tasks. By numerical simulation, this study illustrates that an origami meta-material can also be used as a dynamic reservoir for pattern generation, output modulation, and input sensing. These results could pave the way for intelligently designed origami-based robots that interact with the environment through a distributed network of sensors and actuators. This embodied intelligence will enable the next generations of soft robots to autonomously coordinate and modulate their activities, such as locomotion gait generation and limb manipulation while resisting external disturbances.

The cement-based additive manufacturing, commonly known as 3D concrete printing, facilitates the use of advanced cementitious materials in construction as this construction technique minimizes waste and enables the optimal placement of the material. 3D printable cementitious mixtures should have specific consistency for successful manufacturing. In particular, they should be extruded smoothly during the printing process while maintain their shape after deposition, both of which are closely related to the rheological properties of cementitious mixture. The use of graphene in cementitious composites has been widely explored in recent years and it was shown that graphene can improve the mechanical properties and durability of cementitious composites. However, the rheological properties and printability characteristics of graphene-reinforced cementitious materials still remain underexplored. This study investigates the effects of graphene nanoplatelets (GNPs) on rheological and printability characteristics of GNP-reinforced cementitious composites. GNPs are added into cementitious mixtures, designed for 3D concrete printing applications, at concentration of 0%, 0.05%, 0.10%, 0.15%, 0.20%, and 0.25% by weight of cement. GNPs are first dispersed into water through the help of ultrasonic treatment and a polycarboxylate-based superplasticizer. The dispersion quality of GNPs is assessed through UV-vis absorption spectroscopy, optical microscopy, and Raman spectroscopy. Then, the rheological properties of GNP-reinforced mortar composites are studied using a shear rheometer via stress-growth tests, shear rate ramp up-down tests, and structural recovery tests.

Entropy dynamics is a Bayesian inference methodology that quantifies posterior probability densities and associated phases as a sequence of snap-shots in time to estimate the most likely material particle positions as a function of external stimuli (e.g., heat, traction, electromagnetic fields, chemicals, etc.). The inference method provides a means to create models at the continuum and quantum scales purely based on probability inference. Here we explore its application to fractal structure and fractional properties for polymer mechanics. We investigate how fractal polymer network structure influences the hyper-elastic constitutive behavior for a broad class of polymers such as auxetic foams, dielectric elastomers, and liquid crystal elastomers which can exhibit fractal structure and have applications in the development of adaptive structures.

Certain biological organisms are born with shape, texture, and color morphing skin with the purpose of adapting to their surroundings or morphing their skin for camouflage, signaling, and hunting, among others. The recent demonstrations on artificial surfaces for mimicking biological capabilities, such as dry adhesives on geckos’ feet or the low drag coefficient of sharks’ skin, were achieved by controlling its surface topographies (i.e., shape, size, and distribution of asperities). Similarly, there have been tremendous interests in optimizing artificial surfaces that can continuously morph their surface texture for various applications. While several innovative artificial skins based on mechanical metamaterials have been developed, achieving controllable surface morphing remains challenging. In this study, a Bio-Inspired Active Skin (BIAS) that could selectively change its surface topography was designed and controlled by manipulating its local stress concentrations when subjected to strains. The 3D-printed and thin-film-like BIAS is based on a preconceived auxetic pattern designed to yield a Poisson’s ratio of zero. When strained, these mechanical metamaterials can release stress concentrations in the form of bending and twisting, thereby enabling surface morphing. The main focus of this work was to investigate the geometrical dependence (i.e., width and rib angles) on surface morphing performance, as well as the effects of various designed geometrical imperfections (i.e., notch dimensions and locations) to prevent an uncontrollable and unpredictable morphing response. A slight adjustment in the notch design was enough to change the stress concentration, resulting in various deformed states. The nonlinear response of 3D-printed BIAS was characterized using both experiments and finite element simulations to design the unit cell geometries and to optimize the configurations and locations of the designed imperfections.

The next generation of materials needs to be adaptive, multifunctional and tunable. This goal can be achieved by metamaterials that enable development of advanced artificial materials with novel functionalities. There is arguably a critical shortage in research needed to engineer new aspects of intelligence into the texture of metamaterials for multifunctional applications. The goal of this study is to create a new generation of multifunctional composite mechanical metamaterials called self-aware composite mechanical metamaterial (SCMM) with complex internal structures toward achieving self-sensing and self-powering functionalities. We develop finely tailored and seamlessly integrated microstructures composed of topologically different topologically materials to form a hybrid sensor and nanogenerator mechanical metamaterial system. Experimental studies are conducted to understand the mechanical and electrical behavior of the multifunctional SCMM systems. We highlight how introducing the self-sensing and self-powering functionality into the material design could in theory lay the foundation for living engineered materials and structures that can sense, empower and program themselves using their constituent components.

This Conference Presentation, “Energy transduction versus design characteristics of a magnetoelastic peristalsis pump,” was recorded for the Smart Structures + Nondestructive Evaluation 2021 Digital Forum.

This Conference Presentation, “Large deformation of 3D printed reconfigurable cylindrical shells with multiple stable states,” was recorded for the Smart Structures + Nondestructive Evaluation 2021 Digital Forum.

Triboelectric nanogenerators have received significant research attention in recent years. The energy harvesting performance of triboelectric nanogenerator can be enhanced via more efficient structural and material designs. Here, we develop novel magnetic capsulate triboelectric nanogenerator (MC-TENG) devices to harvest electrical energy under various external excitations. MC-TENG uses a magnetic oscillation system to guide oscillating dielectric capsules within a conductive shield. Steel spring connectors are then utilized to maximize the oscillations and higher power density. Experimental studies is conducted to investigate the electrical performance of MC-TENG under cyclic loading. The output power of the developed nanogenerators reached 400 μW. The proposed MC-TENG concept provides an effective method to harvest electrical energy from low-frequency and low-amplitude oscillations.

Thermoelectric generators (TEGs) have received immense attention in the area of wearable electronics because they offer self-sustainable power sources. Regardless of the past advances in wearable thermoelectric generators, the level of flexibility and durability of such devices has remained limited. Here, we present our recent work on wearable TEGs based on liquid metal (LM) composites that are utilized as thermal material interfaces. These multifunctional layers of LM composites provide conformity with skin and efficiently transfer body heat to thermoelectric modules while they increase heat dissipation on the upper side of the device. The LM composites consist of eutectic gallium indium (EGaIn) inclusions and a silicone-based elastomer matrix (polydimethylsiloxane, PDMS). The embedded EGaIn droplets significantly enhance the thermal conductivity of the silicone matrix which is suitable for heat absorption and dissipation. In the case of EGaIn microdroplets, electrically conductive pathways can be created to function as an integrated circuit board. This is achieved by mechanical sintering of the embedded EGaIn droplets. Moreover, the soft and stretchable nature of the LM composites allows for intimate contact between the hot/cold surfaces (i.e., human skin) and the thermoelectric device. The TEG devices with LM composite show enhanced power generation particularly in wearable devices. Different types of flexible TEGs are fabricated here, and their performance is characterized by thermoelectrical and electromechanical measurements. Lastly, their potential application in self-powered wearable devices is discussed.

Despite being most widely used construction materials, cement-based composites are brittle materials with low tensile strength and susceptible to cracking, especially under harsh environments. Over the past three decades, numerous studies have been conducted to enhance the mechanical properties and durability of cementitious composites through the use of various nanomaterials such as carbon nanotubes (CNTs) and carbon nanofibers (CNFs). More recently, graphene nanoplatelets (GNPs) has emerged as an ideal 2D nano-reinforcement for composite materials due to their favorable mechanical, thermal and electrical properties. However, the effects of different dispersing agents and particle size and surface area of GNPs on the mechanical properties of cement-based composites needs to be further investigated. This paper explores the influence of GNP addition on the mechanical properties and durability of cement-based composites. Two types of GNPs with different lateral size (<2 μm and 25 μm) and specific surface area (300 m²/g and 120 m²/g) are used in this study. The GNP concentration is set to be 0.1% by weight of cement in all mixtures. In order to study the effect of dispersion agents, four different dispersion method are utilized to disperse and stabilize GNP particles in aqueous solution. Compressive strength and flexural strength tests are conducted to assess the mechanical properties, while sorptivity test and surface resistivity measurement are carried out to evaluate the durability. In order to explore the effect of GNPs on hydration process of cement mortar, mechanical properties tests are conducted at 7 day and 28 day curing ages and thermal gravimetric analyses are conducted.

This paper investigates the rheological and printability characteristics of PVA fiber-reinforced cementitious composites. To fabricate 3D printable strain hardening cementitious mixtures, ordinary Portland cement, fly ash, silica fume, fine sand, water, and a polycarboxylate-based superplasticizer are used. The effects of a modified starch-based viscosity modifying agent and nano clay on the rheological properties of these mixtures are explored. A shear rheometer with a building materials cell and vane motor is used for rheological tests. First, stress-growth tests are conducted to determine the static yield stress evolution curves for the PVA fiber-reinforced cement composites. A constant low shear rate is applied to minimize the viscous contributions to yield stress. Then, structural recovery tests are conducted by applying three different shear rates that mimic initial rest, extrusion, and after deposition conditions of printable mixtures and the change in apparent viscosity is observed. Next, structural build-up of PVA fiber-reinforced cementitious composites is assessed through constant shear rate rheology tests at different rest intervals. Finally, the buildability of the PVA fiber-reinforced cementitious composites is evaluated using a 3D concrete printer equipped with a 15 mm diameter nozzle and screw pump.

In recent years, significant research efforts have been dedicated to the development and application of functionally graded materials (FGMs) in the control and manipulation of engineered materials and structures. This study proposes an analytical investigation of the postbuckling behavior of a multi-direction anisotropic FGMs beam subjected to bilateral constraints. The FGMs beam consists of two isotopic layers and is assumed to be graded in the x, y and z directions. Theoretical models are developed to examine the force-displacement relations and the postbuckling shape configurations of the FGMs. Two elastic moduli (i.e., following polynomial and trigonometric functions) are considered to obtain the desired stored potential energy under static axial compressive loading. Here, the FGMs beam’s behavior is represented by a fourth order nonlinear partial differential equation, while the energy minimization technique is employed to solve the governing equation of the mathematical model. Furthermore, the Nelder-mead algorithm and parallel Kernel configuration are used to determine the minimum energy paths of the deformed elastic beam, along with the corresponding snap through events. We compared the proposed model to existing studies in literature, and satisfactory agreements were obtained. Moreover, parametric studies are carried out to assess the influence of varying the material properties (i.e., volume fraction) on the tunable FGMs beam. The results revealed that the material distribution function has a significant effect on the postbuckling response of FGMs beam. Also, the results showed that optimizing material functions lead to better controllability over the FGM beams. The approach presented in this study provides a promising strategy to exploit the performance of FGMs, predicting and maneuvering the postbuckling response for advanced technological devices.

Traditional origami patterns can be applied to pre-strained polystyrene (PSPS) sheets to create precisely fabricated three-dimensional shapes through self-folding. The basis of these folded shapes are the tessellated repeat units, comprising only a few faces and folds. Subtle modifications of the geometry of the patterns allows generation of many final shapes based on the same fundamental repeat unit. These subtle changes allow for variations in attributes like packing density or curvature. Further, self-folded PSPS sheets represent a novel kind of engineering material in which the mechanical properties depend on the fold pattern and the extent of folding . After folding, the adjacent hinges and faces of these structures differ from each other in thickness, temperature history, and orientation relative to loading directions. We seek to characterize the mechanical properties of self-folded, periodic structures to gain a better understanding of how to design and utilize them in engineering applications. Miura-ori patterns will be applied to PSPS sheets, which self-fold in response to infrared light absorption. Folded samples with a range of face sizes and pattern angles will be subjected to compressive testing. Modification of geometric parameters, along with exposure time to the infrared light, has a significant effect on orientation of faces relative to the direction of loading, which will allow control over the final shape and mechanical properties.

This Conference Presentation, “Computational modeling of hot rolling process for biaxial prestraining of shape memory polymer sheets,” was recorded for the Smart Structures + Nondestructive Evaluation 2021 Digital Forum.

Explosively driven ferroelectric generators (FEG) are used as pulsed power sources in many applications that require a compact design that delivers a short high-voltage and high-current pulse. A mechanical shock applied to ferroelectrics releases bound electrical charge through a combination of piezoelectric, domain reorientation, and phase transformation effects. Lead-zirconate-titanate (PZT) 95/5 lies near the ferroelectric (FE)-antiferroelectric (AF) phase boundary and readily transforms to AF phase under compression because AF has a smaller unit volume. This makes it a popular choice for FEGs as the FE-AF transformation completely releases all the stored dipole charge. The complexity of piezoelectric, domain reorientation, and phase transformation behaviors under high deviatoric stress makes modeling this FE to AF transformation and the accompanying charge release challenging. The mode and direction of domain reorientation and phase transformation varies significantly with different deviatoric and hydrostatic stress states. Microstructure changes due to domain reorientation and phase alter the piezoelectric properties of the material. Inaccuracies in modeling any one of these phenomena can result in inaccurate electrical response. This work demonstrates a material model that accurately captures the linear piezoelectric, domain reorientation and phase transformation phenomena by using a micromechanical approach to approximate the changes in domain-structure.

Polymeric lattices offer lightweight structures in which subtle changes in temperature can be used to manipulate lattice shape and mechanical properties. We seek to elicit the interrelations between inhomogeneous deformations, spatially and temporally non-linear mechanical properties, and the resulting mechanical properties of polymeric lattices. Computational modeling allows the measurement of local parameters, e.g. stress gradients and viscous strains, that are difficult or impossible to evaluate experimentally. We implement a coupled thermo-mechanical finite element analysis framework to evaluate lattices subjected to the shape memory cycle. Insight gained from this work advances the understanding of lattice structures with adaptive mechanical properties.

Inconel 718 (IN718) is a nickel-based superalloy that exhibits excellent tensile and impact resistant properties along with good corrosion resistance at high temperatures. Due to the work hardening property of IN718, the machinability of this superalloy is low , which paves a path to adopt the selective laser melting (SLM) process to fabricate IN718. SLM process is governed by process parameters like hatch spacing, scan speed, layer thickness, scan pattern and laser power. This variation in these parameters shall influence the microstructural properties. The various scan patterns adopted for this study are chess, stripes, flow-optimized, and customized scan strategy. These various scan patterns shall cause a variation in the area of the heat-affected zones to change the temperature gradient, which thereby determines the grain size ranging from equiaxed to elongated. There is a difference between the magnitude of thermal gradients generated between the lower layers and top-most layers during the build process. As the microstructure of the part is dependent on the thermal intensity between the layers, it is necessary to study the effect of scan strategy on the microstructure. The study focuses on the effect of the variation in scan patterns on the microstructure of the part.

Selective laser melting (SLM) is the most common additive manufacturing technique designed to fabricate functional parts with high accuracy. Depending on the desired properties, the process parameters for a given material need to be optimized for improving the overall reliability of the SLM devices. As all the process parameters are inter-dependent on each other, it is important to find an optimum value to suit the requirement and render the best build quality. This work primarily focuses on the effect of various process parameters such as laser power, scanning speed, and hatch spacing on the roughness of Inconel 718 parts fabricated on an EOS M290 machine. Statistical models of surface roughness are established to identify the relationship between the abovementioned process parameters. The capabilities developed in this study will permit a deep understanding of the process- property relationships in structural SLM components.

Additive manufacturing (AM) facilitates the fabrication of intricate structures with exceptional engineering characteristics. In this study, selective laser melting (SLM) is used to melt and fuse Ti6Al4V powder using a high-density laser. The use of the laser enables the fabrication of complex parts with high accuracy. The properties of the fabricated part can be customized to fit its application by varying the process parameters such as laser power, scan speed, scan strategy, and hatch spacing. Thus, it is important to optimize these process parameters before fabricating parts for a specific application. The aforementioned process parameters are interdependent on each other and thereby making this process of optimizing the process parameters a vital one. In this study, a full factorial central composite design (CCD) of the response surface methodology (RSM) was used to study the effect of laser power, scan speed, and hatch spacing on the Vickers hardness values. The simulated models obtained using the RSM technique were then studied and thus establish a relationship between these factors.

Inconel 718 (IN718), a nickel-based superalloy, is commonly used in rocket nozzles and turbines. Conventional manufacturing of complex IN718 geometries is difficult due to its high stiffness values. Consequently, Additive Manufacturing (AM) methods like selective laser melting (SLM), offers a viable solution for the fabrication of parts using IN718 with high accuracy. One of the limitations of this technique is its need for supports in order to fabricate overhanging structures. These supports need to be designed carefully and tend to consume a significant amount of resources. In this research, we studied the angled structures fabricated without supports. The overhangs were fabricated using uniform process parameters for varying thicknesses. Microstructural and hardness analyses were carried out to determine variations in melt pools and Vickers hardness. The outcome of this study will help us in predicting the need for supports in overhangs and inclined structures used within a part having complex geometry.

Selective laser melting (SLM) is an additive manufacturing (AM) process capable of fabricating parts of intricate shapes and sizes by melting layers of metal powder with high accuracy. Nickel–Titanium (NiTi) is a shape memory alloy with superelastic characteristics which is of great interest to modern industries. The conventional fabrication of NiTi is limited due the poor machinability of NiTi. Thus, modern manufacturing techniques like SLM enables the fabrication of complex NiTi specimens to suit its application. Nevertheless, microstructural defects such as porosities and microcracks often lead to decreased ductility in SLM fabricated NiTi specimens. In this study, a new technique is used in order to deposit nano metal powders over already formed microcracks, followed by their melting, in order to reduce these microstructural defects. The as-fabricated additive manufactured NiTi sample with the novel nanoparticle dispersion technique exhibited a reduction of 98% in crack density.