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Since a multiaxial loading environment may lead to the fatigue failure of structures, establishing a reliable fatigue model to predict the multiaxial fatigue lives of structures has always been a concern of engineers. This study proposes a new multiaxial fatigue theoretical model (WYT model) based on the critical plane theory, which takes the plane of the maximum shear strain amplitude as the critical plane and considers the effects of shear stress and normal stress on fatigue damage. Moreover, a backpropagation neural network (BPNN) model for multiaxial fatigue life prediction with the shear strain amplitude, normal strain amplitude, mean shear stress, and mean normal stress on the same critical plane as input parameters and fatigue life as the output variable is established. Finally, the WYT model and the BPNN model are compared with two existing multiaxial fatigue models to evaluate the life prediction effects of different models for S45C and 7075-T651 under constant-amplitude and variable-amplitude multiaxial loadings. The calculation results show that the WYT model is feasible, and the BPNN model is more accurate in predicting the fatigue lives of specimens than other multiaxial fatigue theoretical models.

Epoxy resins are a class of viscoelastic hygroscopic polymers widely used in plastic packaged devices. Stress relaxation and yielding may occur in plastic packaged devices under thermal-hygro environments, which seriously affect the reliability of the devices. It is important to obtain accurate viscoelastic parameters of the hygroscopic materials for analyzing the stresses and strains of plastic packaged devices. In this paper, a novel method for measuring the viscoelastic parameters of epoxy resins under water bath environments by using embedded strain gauges is proposed. A kind of epoxy resin-copper bi-layer plate is fabricated, and the biaxial strain gauges are embedded between the epoxy resin layer and the copper layer to monitor the deformations. The interfacial strains of the two test pieces at 85 °C/100% relative humidity are continuously measured up to 144 h. Combine the experimental strain curves and the analytical representation, the viscoelastic parameters of the epoxy resins are obtained by curve fitting with MATLAB and genetic algorithm. It is found that the viscoelasticity of epoxy resins could be described by an exponential function of order five shear modulus and relaxation times, and the interfacial strain curves obtained by genetic algorithm fit well with the experimental ones. The proposed testing and solving methods provide a valuable reference for getting the time-dependent viscoelasticities of epoxy resins under thermal-hygro environments.

A suspended cable–based parallel robot (CBPR) composed of four cables and an end–grab is employed in a pick–and–place operation of moving target gangues (MTGs) with different shapes, sizes, and masses. This paper focuses on two special problems of pick–and–place trajectory planning and trajectory tracking control of the cable–based gangue–sorting robot in the operation space. First, the kinematic and dynamic models for the cable–based gangue–sorting robots are presented in the presence of model uncertainties and unknown external disturbances. Second, to improve the sorting accuracy and efficiency of sorting system with cable–based gangue–sorting robot, a four-phase pick–and–place trajectory planning scheme based on S-shaped acceleration/deceleration algorithm and quintic polynomial trajectory planning method is proposed, and moreover, a robust adaptive fuzzy tracking control strategy is presented against inevitable uncertainties and unknown external disturbances for trajectory tracking control of the cable–based gangue–sorting robot, where the stability of a closed-loop control scheme is proved with Lyapunov stability theory. Finally, the performances of pick–and–place trajectory planning scheme and robust adaptive tracking control strategy are evaluated through different numerical simulations within Matlab software. The simulation results show smoothness and continuity of pick–and–place trajectory for the end–grab as well as the effectiveness and efficiency to guarantee a stable and accurate pick–and–place trajectory tracking process even in the presence of various uncertainties and external disturbances. The pick–and–place trajectory generation scheme and robust adaptive tracking control strategy proposed in this paper lay the foundation for accurate sorting of MTGs with the robot.

This paper presents an approach on stability analysis of cable-driven parallel robots (CDPRs) considering cable mass based on cable tensions and stiffness matrix condition numbers. A cable model is introduced considering the effect of cable mass. A stiffness model is established based on the cable model. Then, the stiffness matrix condition numbers are presented. Furthermore, the stability of CDPRs is discussed. Numerical simulations show that the stability is better in the center and at the top of the whole workspace than in other places, which agrees well with theoretical predictions. The mixed evaluation stability indices considering cable mass and neglecting cable mass are, respectively, compared. The results show that cable mass is not negligible when evaluating accurately the stability of a cable-driven parallel robot.

Since most of the cable-driven parallel manipulators (CDPMs) are small in dimension or low in speed, the self-weight or inertia of the cable is neglected when dealing with the problems of kinematics, dynamics and workspace. The cable is treated as a massless straight line, and the inertia of the cable is not discussed. However, the camera robot is a large-span high-speed CDPM. Thus, the self-weight and inertia of the cable cannot be negligible. The curved cable due to the self-weight is modeled as a catenary to accurately account for its sagging effect. Moreover, the dynamic model of the camera robot is derived by decomposing the motion of the cable into an in-plane motion and an out-plane motion, based on which an iterative-based tension distribution algorithm and a workspace generation algorithm are presented. An optimization model is presented to simultaneously improve the workspace volume, anti-wind disturbance ability and impulse of tensions on the camera and pan–tilt device system (CPTDS) by selecting the proper optimal variables under the linear and nonlinear constraints. An improved genetic algorithm (GA) is proposed, and the simulation results demonstrate that the improved GA offers a stronger ability in global optimization compared to the standard genetic algorithm (SGA). The ideal-point method is employed to avoid the subjective influence of the designer when performing the multi-objective optimization, and a remarkable improvement of the performance is obtained through the optimization. Furthermore, the distribution characteristics of the optimization objects are studied, and some valuable conclusions are summarized, which will provide some valuable references in designing large-span high-speed CDPMs.

Recently, owing to their excellent flexibility and adaptability, skin-like pressure and strain sensors integrated with the human body have the potential for great prospects in healthcare. This review mainly focuses on the representative advances of the flexible pressure and strain sensors for health monitoring in recent years. The review consists of five sections. Firstly, we give a brief introduction of flexible skin-like sensors and their primary demands, and we comprehensively outline the two categories of design strategies for flexible sensors. Secondly, combining the typical sensor structures and their applications in human body monitoring, we summarize the recent development of flexible pressure sensors based on perceptual mechanism, the sensing component, elastic substrate, sensitivity and detection range. Thirdly, the main structure principles and performance characteristic parameters of noteworthy flexible strain sensors are summed up, namely the sensing mechanism, sensitive element, substrate, gauge factor, stretchability, and representative applications for human monitoring. Furthermore, the representations of flexible sensors with the favorable biocompatibility and self-driven properties are introduced. Finally, in conclusion, besides continuously researching how to enhance the flexibility and sensitivity of flexible sensors, their biocompatibility, versatility and durability should also be given sufficient attention, especially for implantable bioelectronics. In addition, the discussion emphasizes the challenges and opportunities of the above highlighted characteristics of novel flexible skin-like sensors.

As a powerful nonparametric Bayesian model, the infinite mixture model has been successfully used in machine learning and computer vision. The success of the infinite mixture model owes to the capability clustering and density estimation. In this paper, we propose a nonparametric Bayesian model for single-image super-resolution. Specifically, we combine the Dirichlet process and Gaussian process regression for estimating the distribution of the training patches and modeling the relationship between the low-resolution and high-resolution patches: 1) the proposed method groups the training patches by utilizing the clustering property of Dirichlet process; 2) the proposed method relates the low-resolution and high-resolution patches by predicting the property of Gaussian process; and 3) the mentioned two points are not independent but jointly learned. Hence, the proposed method can make full use of the nonparametric Bayesian model. First, the Dirichlet process mixture model is used to obtain more accurate clusters for training patches. Second, Gaussian process regression is established on each cluster, and this directly reduces the computational complexity. Finally, the two procedures are learned simultaneously to gain the suitable clusters with the ability of prediction. The parameters can be inferred simply via the Gibbs sampling technique. Thorough super-resolution experiments on various images demonstrate that the proposed method is superior to some state-of-the-art methods.

Loop closure detection is a key module for visual simultaneous localization and mapping (SLAM). Most previous methods for this module have not made full use of the information provided by images, i.e., they have only used the visual appearance or have only considered the spatial relationships of landmarks; the visual, spatial and semantic information have not been fully integrated. In this paper, a robust loop closure detection approach integrating visual–spatial–semantic information is proposed by employing topological graphs and convolutional neural network (CNN) features. Firstly, to reduce mismatches under different viewpoints, semantic topological graphs are introduced to encode the spatial relationships of landmarks, and random walk descriptors are employed to characterize the topological graphs for graph matching. Secondly, dynamic landmarks are eliminated by using semantic information, and distinctive landmarks are selected for loop closure detection, thus alleviating the impact of dynamic scenes. Finally, to ease the effect of appearance changes, the appearance-invariant descriptor of the landmark region is extracted by a pre-trained CNN without the specially designed manual features. The proposed approach weakens the influence of viewpoint changes and dynamic scenes, and extensive experiments conducted on open datasets and a mobile robot demonstrated that the proposed method has more satisfactory performance compared to state-of-the-art methods.

Variable thickness design is capable of yielding excellent electromagnetic (EM) performance for streamlined airborne radomes. The traditional optimization method using evolutionary computation (EC) techniques can yield variable thickness radome (VTR) design with optimal EM performance but uncontrollable thickness profile, leading to manufacturing difficulties. Thickness profile design based on average incident angle is an efficient way for VTR design with smooth thickness profile, whereas the boresight error (BSE) is relatively large. In this work, insertion phase delay (IPD) correction is introduced in the efficient thickness profile design process to realize a better balance between EM performance and thickness profile smoothness of airborne radomes. The thickness profile obtained from average incident angle is adjusted for IPD distribution symmetry with respect to the antenna axis under scan angles with large BSE. Results indicate that the proposed method can effectively improve the BSE characteristics at the expense of slight deterioration of computational efficiency and thickness profile smoothness.

Horizontal docking assembly is a fundamental process in the aerospace assembly, where intelligent measurement and adjustable support systems are urgently needed to achieve higher automation and precision. Thus, a laser scanning approach is employed to obtain the point cloud from a laser scanning sensor. And a method of section profile fitting is put forward to solve the pose parameters from the data cloud acquired by the laser scanning sensor. Firstly, the data is segmented into planar profiles by a series of parallel planes, and ellipse fitting is employed to estimate each center of the section profiles. Secondly, the pose of the part can be obtained through a spatial straight line fitting with these profile centers. However, there may be some interference features on the surface of the parts in the practical assembly process, which will cause negative effects to the measurement. Aiming at the interferences, a robust method improved from M-estimation and RANSAC is proposed to enhance the measurement robustness. The proportion of the inner points in a whole profile point set is set as a judgment criterion to validate each planar profile. Finally, a prototype is fabricated, a series of experiments have been conducted to verify the proposed method.

This article presents a numerical technique for computing the biaxial yield surface of polymer-matrix composites with a given microstructure. Generalized Method of Cells in combination with an Improved Bodner-Partom Viscoplastic model is used to compute the inelastic deformation. The validation of presented model is proved by a fiber Bragg gratings (FBGs) strain test system through uniaxial testing under two different strain rate conditions. On this basis, the manufacturing process thermal residual stress and strain rate effect on the biaxial yield surface of composites are considered. The results show that the effect of thermal residual stress on the biaxial yield response is closely dependent on loading conditions. Moreover, biaxial yield strength tends to increase with the increasing strain rate.

In order to effectively reduce the thermal stresses of Si/GaAs bonding wafers during their annealing process, first of all, based on E. Suhir’s bimaterial thermal stress theory, the thermal stresses in the wafer bonding interfaces are analyzed and the thermal stress distribution formulas are obtained. Then, the thermal stress distribution curves of Si/GaAs bonding interfaces are investigated by finite element method (FEM) and are compared with the results from E. Suhir’s bimaterial thermal stress theory. Finally, some effective strategies are proposed to reduce the thermal stresses in the bonding interfaces.

The cable-based parallel camera robots possess characteristics of solitary way force, redundant actuation, and high-speed mobility. Therefore, research and solution on the stability are a challenging problem. In the available research literatures, there are limited studies to analyze the factors on the system stability for the high-speed cable-based parallel robots. However, when long-span cables and high maneuverability are involved, the effects of cable inertia on the dynamics should be carefully taken up. Thus, in this study, the effects of cable inertia on the stability of the high-speed cable-based parallel camera robots are studied. The dynamic model of the cable-based parallel robot is deduced with cable inertia based on the time-varying cable length dynamic model, the end-effector dynamic model, and the drive system dynamic model. Then, a tracking-control strategy for the camera robot is proposed based on a modified proportion-derivative (PD) forward controller in the end-effector position space. Further, the control strategy is demonstrated based on Lyapunov stability theory. Finally, numerical results show that camera robot is of stable-tracking performance. The control strategy is an effective approach to improve the stability of the camera robot.

This paper focuses on studying the effect of cable tensions and stiffness on the stability of cable-based parallel camera robots. For this purpose, the tension factor and the stiffness factor are defined, and the expression of stability is deduced. A new approach is proposed to calculate the hybrid-stability index with the minimum cable tension and the minimum singular value. Firstly, the kinematic model of a cable-based parallel camera robot is established. Based on the model, the tensions are solved and a tension factor is defined. In order to obtain the tension factor, an optimization of the cable tensions is carried out. Then, an expression of the system's stiffness is deduced and a stiffness factor is defined. Furthermore, an approach to evaluate the stability of the cable-based camera robots with hybrid tension-stiffness properties is presented. Finally, a typical three-degree-of-freedom cable-based parallel camera robot with four cables is studied as a numerical example. The simulation results show that the approach is both reasonable and effective.

Composite laminates can exhibit the nonlinear properties due to the fiber/matrix interface debonding and matrix plastic deformation. In this paper, by incorporating the interface stress-displacement relations between fibers and matrix, as well as the viscoplastic constitutive model for describing plastic behaviors of matrix materials, a micromechanical model is used to investigate the failure strength of the composites with imperfect interface bonding. Meanwhile, the classic laminate theory, which provides the relation between micro- and macroscale responses for composite laminates, is employed. Theory results show good consistency with the experimental data under unidirectional tensile conditions at both 23°C and 650°C. On this basis, the interface debonding influences on the failure strength of the [0/90]s and [0/±45/90]s composite laminates are studied. The numerical results show that all of the unidirectional (UD) laminates with imperfect interface bonding provide a sharp decrease in failure strength in the σxx-σyy plane at 23°C. However, the decreasing is restricted in some specific region. In addition, for [0/90]s and [0/±45/90]s composite laminates, the debonding interface influences on the failure envelope can be ignored when the working temperature is increased to 650°C.

Camera robots are high-speed redundantly cable-driven parallel manipulators that realize the aerial panoramic photographing. When long-span cables and high maneuverability are involved, the effects of cable sags and inertias on the dynamics must be carefully dealt with. This paper is devoted to the optimal cable tension distribution (OCTD for short) of the camera robots. Firstly, each fast varying-length cable is discretized into some nodes for computing the cable inertias. Secondly, the dynamic equation integrated with the cable inertias is set up regarding the large-span cables as catenaries. Thirdly, an iterative optimization algorithm is introduced for the cable tension distribution by using the dynamic equation and sag-to-span ratios as constraint conditions. Finally, numerical examples are presented to demonstrate the effects of cable sags and inertias on determining tensions. The results justify the convergence and effectiveness of the algorithm. In addition, the results show that it is necessary to take the cable sags and inertias into consideration for the large-span manipulators.

This paper deals with the kinematic calibration and motion control of a triple-level spatial positioner consisting of the cable-driven parallel manipulator (CDPM), active gyro stabilizer (AGS), and the Stewart platform. A six-degree-of-freedom laser tracker is employed when calibrating the benchmark positions and measuring the real-time position and orientation in motion control, which makes it a straightforward solution to tackle with hierarchical mechatronic system actuated by servomotors with incremental encoders. Then the trajectory planning and motion control of the triple-level robotic spatial positioner are explored to verify the correctness and to what extent the calibration improves the system. This CDPM based spatial positioner has an accuracy of several millimeters though it has a ten-meter workspace.

This paper investigates the minimum cable tension distributions in the workspace for cable-based parallel robots to find out more information on the stability. First, the kinematic model of a cable-based parallel robot is derived based on the wrench matrix. Then, a noniterative polynomial-based optimization algorithm with the proper optimal objective function is presented based on the convex optimization theory, in which the minimum cable tension at any pose is determined. Additionally, three performance indices are proposed to show the distributions of the minimum cable tensions in a specified region of the workspace. An important thing is that the three performance indices can be used to evaluate the stability of the cable-based parallel robots. Furthermore, a new workspace, the Specified Minimum Cable Tension Workspace (SMCTW), is introduced, within which all the minimum tensions exceed a specified value, therefore meeting the specified stability requirement. Finally, a camera robot parallel driven by four cables for aerial panoramic photographing is selected to illustrate the distributions of the minimum cable tensions in the workspace and the relationship between the three performance indices and the stability.

We study a nonmonotone adaptive Barzilai-Borwein gradient algorithm for l1-norm minimization problems arising from compressed sensing. At each iteration, the generated search direction enjoys descent property and can be easily derived by minimizing a local approximal quadratic model and simultaneously taking the favorable structure of the l1-norm. Under some suitable conditions, its global convergence result could be established. Numerical results illustrate that the proposed method is promising and competitive with the existing algorithms NBBL1 and TwIST.

This work improved upon an effective micromechanical method to analyze the mechanical properties of three-dimensional particle-reinforced composites (PRC) with consideration of the interfacial debonding. By incorporating the interfacial debonding model, Mises yield criterion, and failure theory, the effects of particle shape, particle volume fraction, and loading condition on the mechanical properties are studied. A comparison of simulation results obtained from the established method and published experimental data is presented. Good consistency can be found in this study. On this basis, the interfacial cohesive strength and particle shape effects on the biaxial failure strength of particle-reinforced composites with interfacial debonding were also studied. The results revealed that both interfacial strength and particle shape have significant effects on biaxial tensile failure strength. However, the different interfacial strength influence on failure envelope can hardly be discerned in biaxial compressive loading.

Output prediction is one of the difficult issues in production management. To overcome this difficulty, a dynamic-improved multiple linear regression model based on parameter evaluation using discrete Hopfield neural networks (DHNN) is presented. First, a traditional multiple linear regression model is established; this model takes the factors in production lifecycle (not only one phase of the production) into account, such as manufacturing resources, manufacturing process, and product rejection rate, so it makes the output prediction be more accurate. Then a static-improved model is built using the backstepping method. Finally, we obtain the dynamic-improved model based on parameter evaluation using DHNN. These three models are applied to an aviation manufacturing enterprise based on the actual data, and the results of the output prediction show that the models have practical value.

Cable-driven parallel manipulators are one of the best solutions to achieving large workspace since flexible cables can be easily stored on reels. However, due to the negligible flexural stiffness of cables, long cables will unavoidably vibrate during operation for large workspace applications. In this paper a finite element model for cable-driven parallel manipulators is proposed to mimic small amplitude vibration of cables around their desired position. Output feedback of the cable tension variation at the end of the end-effector is utilized to design the vibration attenuation controller which aims at attenuating the vibration of cables by slightly varying the cable length, thus decreasing its effect on the end-effector. When cable vibration is attenuated, motion controller could be designed for implementing precise large motion to track given trajectories. A numerical example is presented to demonstrate the dynamic model and the control algorithm.

This paper presents a hybrid spectral conjugate gradient method for large-scale unconstrained optimization, which possesses a self-adjusting property. Under the standard Wolfe conditions, its global convergence result is established. Preliminary numerical results are reported on a set of large-scale problems in CUTEr to show the convergence and efficiency of the proposed method.

How cooperation emerges and is stabilized has been a puzzling problem to biologists and sociologists since Darwin. One of the possible answers to this problem lies in the mobility patterns. These mobility patterns in previous works are either random-like or driven by payoff-related properties such as fitness, aspiration, or expectation. Here we address another force which drives us to move from place to place: reputation. To this end, we propose a reputation-based model to explore the effect of migration on cooperation in the contest of the prisoner's dilemma. In this model, individuals earn their reputation scores through previous cooperative behaviors. An individual tends to migrate to a new place if he has a neighborhood of low reputation. We show that cooperation is promoted for relatively large population density and not very large temptation to defect. A higher mobility sensitivity to reputation is always better for cooperation. A longer reputation memory favors cooperation, provided that the corresponding mobility sensitivity to reputation is strong enough. The microscopic perception of the effect of this mechanism is also given. Our results may shed some light on the role played by migration in the emergence and persistence of cooperation.