Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends

Jiao, Pengcheng; Alavi, Amir H.

doi:10.1080/09506608.2020.1815394

Cited by 84 publications

(44 citation statements)

References 339 publications

(625 reference statements)

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“…For example, organic electronic devices based on organic thermoelectric materials [ 46 ], a modified active disturbance rejection control (ADRC) [ 47 ] and temperature-based microscopic temperature control of sensitive upconversion nanocomposite [ 48 ]. Besides, the single-chip microcomputer is also widely used as an advanced temperature control method [ 49 ].…”

Section: Resultsmentioning

confidence: 99%

Self-Triggered Thermomechanical Metamaterials with Asymmetric Structures for Programmable Response under Thermal Excitations

et al. 2021

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In this study, we propose self-triggered thermomechanical metamaterials (ST-MM) by applying thermomechanical materials in mechanical metamaterials designed with asymmetric structures (i.e., microstructural hexagons and chiral legs). The thermomechanical metamaterials are observed with programmable mechanical response under thermal excitations, which are used in mechanical metamaterials to obtain chiral tubes with negative Poisson’s ratio and microgrippers with temperature-induced grabbing response. Theoretical and numerical models are developed to analyze the thermomechanical response of the ST-MM from the material and structural perspectives. Finally, we envision advanced applications of the ST-MM as chiral stents and thermoresponsive microgrippers with maximum grabbing force of approximately 101.7 N. The emerging ST-MM provide a promising direction for the design and perception of smart mechanical metamaterials.

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Section: Resultsmentioning

confidence: 99%

Self-Triggered Thermomechanical Metamaterials with Asymmetric Structures for Programmable Response under Thermal Excitations

et al. 2021

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“…然而, 机械超材料中微观结构的复杂性和多样性导致了设计局部几何结构的困难, 特别是为了要实现某些特定的机械性能时. 人工智能技术是解决机械超材料设计和优化挑战的有效方法 [113] . 采用人工智能技术进行数据挖掘、处理和分析, 可以大大提高超材料结构设计的准确性和效率.…”

Section: 结构物理智能机械超材料通过各种巧妙的微观结构设计获得了不同寻常的物理和机械性能在剪纸结构、unclassified

Bio-inspired physical intelligence for soft robotics

Wang¹,

Xie²,

Yuan³

et al. 2022

Chin. Sci. Bull.

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and structure, self-learning, self-healing, and self-decision-making into physical bodies of the biological or robotic agents. Physical intelligence can also be generated during the interaction between the agent's body and the environment over time. The previous focus of intelligent robots is primarily focused on the computational intelligence. As a new paradigm, physical intelligence is expected to boost intelligent robots in real-world applications.Soft robots commonly use soft stimuli-responsive materials and intelligent structures and maintain high stretchability and considerable deformation, therefore, have intrinsic environmental conformable physical property. Thus, soft robots are essential platforms for testing hypothesis of physical intelligence in nature. This review mainly focused on three key physical intelligence elements encoded in the natural organisms' bodies: material, structure, and morphology. Through bio-inspired design, smart soft materials, smart soft structures, and adaptable morphologies can be integrated into the robot's body, thus introducing bio-inspired physical intelligence into the robots. By integrating bio-inspired physical intelligence, soft robots could reduce the cost of control, improve the response speed of the systems, enhance the robustness of robots in extreme environments, and embed intelligence into the micro-and small-scale robots. The research on bio-inspired physical intelligence A c c e p t e d https://engine.scichina.com/doi/10.1360/TB-2021-1217 may also promote the multi-disciplinary collaboration of biology, robotics, materials science, chemistry, computer science, etc.In this review, we first describe the characteristics and principles of physical intelligence in natural organisms' material, structure, and morphology. Then we introduce the purposes and related key technologies and methods of realizing bio-inspired physical intelligence of soft robots. Furthermore, we enumerate the typical applications of bio-inspired physical intelligence of soft robots. Finally, we highlight the trends and challenges of bio-inspired physical intelligence of soft robots in the future.The research on bio-inspired physical intelligence for soft robots is still in the primary stage. There are several critical questions and challenges to be addressed. First, researchers should investigate the basic principles of physical intelligence in biomechanics and then apply the basic principles to guide the development of soft robots. Second, intelligent materials need to meet the challenges of fully integrating sensing, actuation, memory, computation, and communication in soft robots. To this end, an interesting approach is to use stimulus-responsive materials as the basic building blocks to rationally design and integrate different functions into a single composite material. Third, smart structures, like mechanical metamaterials can be a promising research direction in the field of intelligent structures for soft robots. Aided by artificial intelligence and 3D printing, mechanical metamateria...

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“…ML can also aid the manufacturing process by assisting in the selection of parameters for specific processes [30], allowing the automation of the design of some actuators [31]. The application of hybrid ML to select design parameters in modern mechanical applications, such as in mechanical metamaterials, has been reported [31][32][33][34][35]. Other applications of ML in the structural sector are found in the works of Fu et al [36,37], where the weighted averaging of non-fine-tuned ML models was applied to the concrete construction sector, with special emphasis on corroded beams.…”

Section: Machine Learning Applications In Mechanicsmentioning

confidence: 99%

Compliant Cross-Axis Joints: A Tailoring Displacement Range Approach via Lattice Flexures and Machine Learning

2022

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Compliant joints are flexible elements that allow displacement due to the elastic deformations they experience under the action of external loading. The flexible parts responsible for these displacements are known as flexure hinges. Displacement, or motion range, in compliant joints depends on the stiffness of the flexure hinges and can be tailored through various parameters, including the overall dimensions, the base material, and the distribution within the hinge. Considering the distribution, we propose the stiffness modification of a compliant cross-axis joint via the use of lattice mechanical metamaterials. Due to the wide range of parameters that influence the stiffness of a lattice, different machine learning algorithms (artificial neural networks, support vector machine, and Gaussian progress regression) were proposed to forecast these parameters. Here, the machine learning algorithm with the best forecasting was the Gaussian progress regression; this algorithm has the advantage of well-tuning even with small regression databases, allowing these functions to more easily adjust to suit specific data, even if the dataset is small. Hexagonal, re-entrant, and square lattices were studied as flexure hinges. For each, the effect of the unit cell size and its orientation with respect to the principal axis on the effective stiffness were studied via computational and laboratory experiments on additively manufactured samples. Finite element predictions resulted in good agreement with the experimentally obtained data. As a result, using lattice-flexure hinges led to increments in displacement ranging from double to ten times those obtained with solid hinges. The most suitable machine learning algorithm was the Gaussian progress regression, with a maximum error of 0.12% when compared to the finite element analysis results.

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Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends

Cited by 84 publications

References 339 publications

Self-Triggered Thermomechanical Metamaterials with Asymmetric Structures for Programmable Response under Thermal Excitations

Self-Triggered Thermomechanical Metamaterials with Asymmetric Structures for Programmable Response under Thermal Excitations

Bio-inspired physical intelligence for soft robotics

Compliant Cross-Axis Joints: A Tailoring Displacement Range Approach via Lattice Flexures and Machine Learning

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