The ceaseless quest to realize novel classes of functional materials has provided new road maps to material autonomy. Autonomous materials and structures offer advanced functionalities such as sensing, actuation, selfhealing, communication, and computing to create a sense–decide–respond loop. They have numerous applications in robotics, human-machine interfacing, micro/nano-electromechanical systems, and flexible electronics. During the past decades, tremendous effort has been made to push the development of autonomous materials and structures. This paper presents an overview of the recent progresses, challenges and futures trends in autonomous materials and structures. In the area of material autonomy, active multifunctional metamaterials appear to open enormous field of study. Their scalability is an important feature to create building blocks for multiscale autonomous structures. Thus, this review paper provides an insight into their developments and future trends. The foreseeable challenges are further discussed.
KEYWORDS: Metamaterials, Logic devices, Circuit switching, Digital electronics, Design and modelling, Data processing, Energy harvesting, Computing systems, Materials properties, Signal processing
Active mechanical metamaterials have shown a glimpse of their capacity to create the foundation for intelligent matter. This study presents the concept of mechanical metamaterial electronics (meta-mechanotronics) to design intelligent matter with information processing capability. This advanced functionality is achieved by fusing the mechanical metamaterials, digital electronics and nano energy harvesting technologies. Electronic mechanical metamaterials explored under the meta-mechanotronics paradigm rely merely on their constituent components to perform self-powered mechanical-electrical-logic operations. A proof-of-concept digital unit cell is presented as the 2-bit building block for electronic mechanical metamaterials. The digital unit cell is rationally designed as a monostable origami-inspired metamaterial with twist buckling behavior and specific multi-motion properties to synthesize discrete mechanical configurations and realize digital logic gates. Experimental studies are performed to evaluate the digital computing performance of the designed mechanical metamaterial logic gate.
Natural evolution has been a major source of inspiration for scientists for decades. Here, we aim to harness the power of natural evolution for automated design and discovery of novel forms of metastructures. In the proposed process, evolution takes place by randomly creating an initial population of metamaterial entities that will pass on their genetic material to their offspring through variation, reproduction and selection. The metamaterial configurations with desired response emerge during this evolutionary process. We deploy this process to design metamaterial artefacts assessed during evolution with respect to minimum Poisson’s ratio. The experimental properties of the evolved metamaterial systems are in an acceptable agreement with the theoretical values. With the growing interesting in metamaterials for various engineering applications, artificial evolution of metamaterials could open up new avenues towards more efficient and creative design of these systems.
We demonstrate our striking vision towards developing a new generation of multifunctional concrete materials with unprecedented mechanical properties. The proposed “metamaterial concrete” is based on the integration of snapping metamaterial and concrete design concepts. We show how integrating the concrete mixture with auxetic polymer structures with snap-through buckling behavior results in creating a concrete material system with new functionalities. The developed proof-of-concept prototypes are experimentally tested to verify the efficient of the proposed concept. The results are in a reasonable agreement with the numerical simulations. We discuss the potential of the metamaterial concrete systems in revolutionizing the concrete construction via supplementing the inherent weaknesses of concrete in fatigue applications. The composite constituency of the metamaterial concrete shows levels of compressibility, while maintaining a high level of stiffness. The compressibility of this material reveals the future for a ductile concrete with significantly high flexural capacity and ability to absorb vibration without incurring any flaws.
In this study, we investigate the feasibility of a self-powered Fowler-Nordheim (FN) sensor-data-logger for postoperative monitoring of spinal fusion progress. The FN sensor-data-logger self-powers itself using the energy harvested by a piezoelectric transducer attached to the spinal fixation device. The same signal is then used by the sensor to infer the fusion progress. We perform experimental studies using corpectomy models to evaluate the performance of the proposed monitoring system. Data measured from the bench-top experiments is used to obtain time-evolution curves representing each spinal fusion state. This feasibility study shows that the obtained curves are viable tools to differentiate between conditions of osseous union and assess the effective fusion period.
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.
We present a two-stage method for detection and quantification of surface defects in concrete bridge decks using a hybrid deep learning and image processing technique. In the first stage, a multi classifier based on an integrated convolutional neural network and long short-term memory architecture is developed to detect cracking and spalling regions. A new algorithm based on denoising and nearest neighbor methods is then developed to quantify the crack length within the detected cracking regions. The proposed method offers an acceptable damage detection and quantification performance on rough concrete surfaces. We highlight various aspects of a software program developed using the proposed method for autonomous inspection of bridge and pavement systems.
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