Four metallic metamaterials with tailorable mechanical properties are designed using bi-material star-shaped re-entrant planar lattice structures, which do not involve pins, adhesive, welding or ...pressure-fit joints and can be fabricated through laser-based additive manufacturing. Three length parameters, one angle parameter and three material combinations are used as adjustable design parameters to explore structure-property relations. For each of the four designed metamaterials, the effects of the design parameters on the Poisson’s ratio (PR), coefficient of thermal expansion (CTE), Young’s modulus and relative density are systematically investigated using unit cell-based finite element simulations that incorporate periodic boundary conditions. It is found that the bi-material lattice structures can be tailored to obtain 3-D printable metallic metamaterials with positive, near-zero or negative PR and CTE together with an uncompromised Young’s modulus. In particular, it is shown that metamaterial # 1 can exhibit both a negative PR and a non-positive CTE simultaneously. These metallic metamaterials can find applications in structures or devices such as antennas and precision instruments to reduce thermomechanical stresses and extend service lives.
The coexistence of gate-tunable superconducting, magnetic and topological orders in magic-angle twisted bilayer graphene provides opportunities for the creation of hybrid Josephson junctions. Here we ...report the fabrication of gate-defined symmetry-broken Josephson junctions in magic-angle twisted bilayer graphene, where the weak link is gate-tuned close to the correlated insulator state with a moiré filling factor of υ = -2. We observe a phase-shifted and asymmetric Fraunhofer pattern with a pronounced magnetic hysteresis. Our theoretical calculations of the junction weak link-with valley polarization and orbital magnetization-explain most of these unconventional features. The effects persist up to the critical temperature of 3.5 K, with magnetic hysteresis observed below 800 mK. We show how the combination of magnetization and its current-induced magnetization switching allows us to realise a programmable zero-field superconducting diode. Our results represent a major advance towards the creation of future superconducting quantum electronic devices.
A new Timoshenko beam model is developed using a modified couple stress theory and a surface elasticity theory. A variational formulation based on Hamilton’s principle is employed, which leads to the ...simultaneous determination of the equations of motion and complete boundary conditions for a Timoshenko beam. The new model contains a material length scale parameter accounting for the microstructure effect in the bulk of the beam and three surface elasticity constants describing the mechanical behavior of the beam surface layer. The inclusion of these additional material constants enables the new model to capture the microstructure-and surface energy-dependent size effect. In addition, both bending and axial deformations are considered, and the Poisson effect is incorporated in the current model, unlike existing Timoshenko beam models. The new beam model includes the models considering only the microstructure dependence or the surface energy effect as limiting cases and recovers the Bernoulli–Euler beam model incorporating the two effects as a special case. Also, the current model reduces to the classical Timoshenko beam model when the microstructure dependence, surface energy and Poisson’s effect are all suppressed. To demonstrate the new model, the static bending and free vibration problems of a simply supported beam are analytically solved by directly applying the general formulas derived. The numerical results for the static bending problem reveal that both the deflection and rotation of the simply supported beam predicted by the new model are smaller than those predicted by the classical Timoshenko beam model. In addition, the differences in both the deflection and rotation predicted by the two models are very large when the beam thickness is small, but they are diminishing with the increase of the beam thickness. Similar trends are observed for the free vibration problem, where it is shown that the natural frequency predicted by the new model is higher than that given by the classical model, with the difference between them being significantly large for very thin beams. These predicted trends of the size effect in beam bending at the micron scale agree with those observed experimentally.
•Four types of three-dimensional (3-D) metallic metamaterials with tailorable thermo-mechanical properties are designed. For the first three types, the structure-property relations are studied by ...adjusting design parameters including two length parameters, one angle parameter and two material combinations. For the fourth type, one additional angle parameter is involved.•It is shown that each of the four types of metamaterials designed exhibits the cubic symmetry and thus needs three independent elastic constants to characterize its elastic behavior and one coefficient of thermal expansion to describe its isotropic thermal expansion.•The effects of the design parameters on the effective Poisson's ratio (PR), coefficient of thermal expansion (CTE), Young's modulus, shear modulus and the relative density are systematically investigated for each of the four types of designed metamaterials by using unit cell-based finite element simulations that incorporate periodic boundary conditions.•It is found that 3-D metallic metamaterials with positive, near-zero or negative PR and CTE can be obtained by tailoring the bi-material lattice structures and material combinations. Also, it is revealed that metamaterial # 1 can achieve both a negative PR and a non-positive CTE while maintaining a high stiffness and a low relative density (and thus a lightweight).•The good tunability of thermo-mechanical properties of the four types of metamaterials provides an avenue of enabling the expansion of Ashby's material chart to produce more material options for engineering applications.
Four types of three-dimensional (3-D) metallic metamaterials with tailorable thermo-mechanical properties are designed. For the first three types, the structure-property relations are studied by adjusting design parameters including two length parameters, one angle parameter and two material combinations. For the fourth type, one additional angle parameter is involved. It is shown that each of the four types of metamaterials designed exhibits the cubic symmetry and thus needs three independent elastic constants to characterize its elastic behavior and one coefficient of thermal expansion to describe its isotropic thermal expansion. The effects of the design parameters on the effective Poisson's ratio (PR), coefficient of thermal expansion (CTE), Young's modulus, shear modulus and the relative density are systematically investigated for each of the four types of designed metamaterials by using unit cell-based finite element simulations that incorporate periodic boundary conditions. It is found that 3-D metallic metamaterials with positive, near-zero or negative PR and CTE can be obtained by tailoring the bi-material lattice structures and material combinations. Also, it is revealed that metamaterial # 1 can achieve both a negative PR and a non-positive CTE while maintaining a high stiffness and a low relative density (and thus a lightweight). The good tunability of thermo-mechanical properties of the four types of metamaterials provides an avenue of enabling the expansion of Ashby's material chart to produce more material options for engineering applications.
•Evolvement of deep learning technologies and their advantages over traditional machine learning are discussed.•Computational methods based on deep learning are presented to improve system ...performance.•Emerging topics and future trends of deep learning for smart manufacturing are summarized.
Smart manufacturing refers to using advanced data analytics to complement physical science for improving system performance and decision making. With the widespread deployment of sensors and Internet of Things, there is an increasing need of handling big manufacturing data characterized by high volume, high velocity, and high variety. Deep learning provides advanced analytics tools for processing and analysing big manufacturing data. This paper presents a comprehensive survey of commonly used deep learning algorithms and discusses their applications toward making manufacturing “smart”. The evolvement of deep learning technologies and their advantages over traditional machine learning are firstly discussed. Subsequently, computational methods based on deep learning are presented specially aim to improve system performance in manufacturing. Several representative deep learning models are comparably discussed. Finally, emerging topics of research on deep learning are highlighted, and future trends and challenges associated with deep learning for smart manufacturing are summarized.
•A new analytical model is developed for three types of 2-D periodic star-shaped re-entrant lattice structures that possess the orthotropic symmetry and exhibit negative Poisson's ...ratios.•Contributions from both the re-entrant and connection struts are considered using an energy method based on Castigliano's second theorem. Each re-entrant strut is treated as a Timoshenko beam, and stretching, transverse shearing and bending deformations are all incorporated in the formulation.•Unlike existing studies, the overlapping of struts at joints is included in determining the relative density, which is analytically expressed for each lattice type.•Closed-form formulas are derived for the effective Young's moduli and Poisson's ratios of each type of lattice structure, which contains three non-dimensional length ratios, two re-entrant angles, one shear correction factor, and Young's modulus and Poisson's ratio of the strut material.•The new analytical model is validated against finite element simulations conducted in the current study and two existing analytical models for simpler square lattice structures without re-entrant struts.•It is demonstrated that through a proper selection of the geometrical parameters, vertex connections and strut material, it is possible to tailor the effective Poisson's ratios and Young's moduli of each type of lattice structure over wide ranges to satisfy different needs in various applications.
A new analytical model is developed for three types of 2-D periodic star-shaped re-entrant lattice structures that possess the orthotropic symmetry and exhibit negative Poisson's ratios. Contributions from both the re-entrant and connection struts are considered using an energy method based on Castigliano's second theorem. Each re-entrant strut is treated as a Timoshenko beam, and stretching, transverse shearing and bending deformations are all incorporated in the formulation. Unlike existing studies, the overlapping of struts at joints is included in determining the relative density, which is analytically expressed for each lattice type. Closed-form formulas are derived for the effective Young's moduli and Poisson's ratios of each type of lattice structure, which contain three non-dimensional length ratios, two re-entrant angles, one shear correction factor, and Young's modulus and Poisson's ratio of the strut material. The new analytical model is validated against finite element simulations conducted in the current study and two existing analytical models for simpler square lattice structures without re-entrant struts. To illustrate the newly developed analytical model, a parametric study is conducted to quantitatively show the effects of the five geometrical parameters on the effective properties of each type of lattice structure. It is found that for the effective Poisson's ratios the key controlling parameters are the two re-entrant angles, while for the effective Young's moduli all of the geometrical parameters can have significant effects except for the external connection length ratio. It is demonstrated that through a proper selection of the geometrical parameters, vertex connections and strut material, it is possible to tailor the effective Poisson's ratios and Young's moduli of each type of lattice structure over wide ranges to satisfy different needs in various applications.
Display omitted A new analytical model is developed for three types of 2-D periodic star-shaped re-entrant, lattice structures (with their unit cells shown in Fig. 1) that possess the orthotropic symmetry and, exhibit negative Poisson's ratios.
•We conduct a detailed review of the applications of recent deep learning models on machine health monitoring tasks and provide our own insights into these models.•Practical studies about ...conventional machine learning models and deep learning models on a challenging tool wear prediction have been given. Related data and code have also been open to public.•We present current deep learning works on machine health monitoring in a well-organized way to facilitate researchers to catch this topic and provide discussions about the future direction in this research topic.
Since 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide range of areas such as object recognition, image segmentation, speech recognition and machine translation. In modern manufacturing systems, data-driven machine health monitoring is gaining in popularity due to the widespread deployment of low-cost sensors and their connection to the Internet. Meanwhile, deep learning provides useful tools for processing and analyzing these big machinery data. The main purpose of this paper is to review and summarize the emerging research work of deep learning on machine health monitoring. After the brief introduction of deep learning techniques, the applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder (AE) and its variants, Restricted Boltzmann Machines and its variants including Deep Belief Network (DBN) and Deep Boltzmann Machines (DBM), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). In addition, an experimental study on the performances of these approaches has been conducted, in which the data and code have been online. Finally, some new trends of DL-based machine health monitoring methods are discussed.
A non-classical Mindlin plate model is developed using a modified couple stress theory. The equations of motion and boundary conditions are obtained simultaneously through a variational formulation ...based on Hamilton’s principle. The new model contains a material length scale parameter and can capture the size effect, unlike the classical Mindlin plate theory. In addition, the current model considers both stretching and bending of the plate, which differs from the classical Mindlin plate model. It is shown that the newly developed Mindlin plate model recovers the non-classical Timoshenko beam model based on the modified couple stress theory as a special case. Also, the current non-classical plate model reduces to the Mindlin plate model based on classical elasticity when the material length scale parameter is set to be zero. To illustrate the new Mindlin plate model, analytical solutions for the static bending and free vibration problems of a simply supported plate are obtained by directly applying the general forms of the governing equations and boundary conditions of the model. The numerical results show that the deflection and rotations predicted by the new model are smaller than those predicted by the classical Mindlin plate model, while the natural frequency of the plate predicted by the former is higher than that by the latter. It is further seen that the differences between the two sets of predicted values are significantly large when the plate thickness is small, but they are diminishing with increasing plate thickness.
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