Mechanical properties of carbon nanotube reinforced polymer nanocomposites: A coarse-grained model

In this work, a coarse-grained (CG) model of carbon nanotube (CNT) reinforced polymer matrix composites is developed. A distinguishing feature of the CG model is the ability to capture interactions between polymer chains and nanotubes. The CG potentials for nanotubes and polymer chains are calibrated using the strain energy conservation between CG models and full atomistic systems. The applicability and efficiency of the CG model in predicting the elastic properties of CNT/polymer composites are evaluated through verification processes with molecular simulations. The simulation results reveal that the CG model is able to estimate the mechanical properties of the nanocomposites with high accuracy and low computational cost. The effect of the volume fraction of CNT reinforcements on the Young's modulus of the nanocomposites is investigated. The application of the method in the modeling of large unit cells with randomly distributed CNT reinforcements is examined. The established CG model will enable the simulation of reinforced polymer matrix composites across a wide range of length scales from nano to mesoscale.     

Eco-friendly fermentation module for maximization of hydrogen harvesting from fatty restaurant waste diluted with grey water

Valorization of fatty restaurant waste (RW) is an economically and environmentally promising approach due to the enriched high biodegradable organic content of this substrate. However, the lipid fraction has an adverse impact on the hydrogen fermentation process. Thus, three staged hybrid reactors (HR1, HR2 and HR3) connected in series were investigated for lipid degradation and H₂ harvesting from the codigestion of fatty RW and grey water. A hydrogen harvesting volume of 0.3 ± 0.02 L/L.d., was recorded in HR1, and lower values of 0.22 ± 0.1 and 0.11 ± 0.06 LH₂/L.d were obtained in HR2 and HR3, respectively. The phyla Actinobacteria (6.4%), Bacteroidetes (10.9%), Chloroflexi (45.1), and Proteobacteria (22.4%), which degrade carbohydrates, lipids and proteins, were dominant in HR1 and highly reduced in HR2 and HR3. Nevertheless, the abundance of the phylum Firmicutes was 16.6% in HR1 and increased to 21.7% in HR3. Staging of the anaerobic digesters highly distributed the hydrolytic and acetogenic syntrophic microbes, creating a unique fermentation medium for H₂ harvesting from RW diluted with grey water.     

改进的三频三步相移结构光三维重建方法

针对结构光三维重建中的传统三频三步相移方法需要投影过多编码图像、效率低的问题,提出了改进的三频三步相移结构光三维重建方法。该方法与传统三频三步方法均需要3种频率的正弦条纹图,但条纹图数量只需5张,即最高频率的条纹图3张,但初始相位不同,剩余频率的条纹图各1张。较传统方法的9张条纹图投影效率提高44.44%。随后推导了该方法的求解原理,由最高频的3张条纹图直接求得重建所需包裹相位,另外两张条纹图用于对包裹相位进行展开,理论上该方法与传统三频三步算法具有相同的精度。最后进行了4组实验,验证了该方法的重建精度、复杂不连续模型重建能力、不同光照环境中的重建稳定性以及对彩色物体重建能力。结果证明该方法在有效提高测量速度的同时保证了和传统三频三步方法一致的精度和适应性。     

3D视觉结合图像检测与导纳控制的圆轴孔零件机器人装配

面向机器人柔顺装配圆轴与圆孔零件,建立基于3D、单目视觉与导纳控制的机器人自动装配系统,提出基于三维点云的轴线位姿估计算法、图像深度学习目标检测、导纳控制结合的圆轴孔零件的装配策略.针对3D视觉估计圆孔零件位姿问题,重点研究基于三维点云的轴线位姿估计算法.首先,介绍三维点云关键点选取方法;然后,以点云表面法线与轴线的几何约束为基础,提出并分析轴线粗估计的算法;最后,在轴线粗估计的基础上,提出并分析基于迭代鲁棒最小二乘的轴线位姿优化的算法.实验结果表明:轴线位姿估计的角度均方根误差为0.248°,位置均方根误差为0.463 mm,与现有流行的轴线估计方法相比,所提方法的精度更高,使装配策略很好地满足了机器人圆形轴孔零件装配的精度高、稳定可靠的要求.     

In-situ toughened CFRP composites by shear-calender orientation and fiber-bundle filtration of PA microparticles at prepreg interlayer

Epoxy matrix toughened by polyethersulfone (PES) and polyamide (PA) microparticles was designed and the in-situ interlaminar toughened carbon fiber/epoxy composites were fabricated. Synergistic toughening effect of PES and PA on epoxy matrix was achieved due to semi-IPN structure of PES toughened matrix and uniform dispersion of PA microparticles. Shear-calender orientation of PA microparticles was found during prepreg processing and the microparticles remained on the surface of prepreg due to fiber-bundle filtration. The in-situ formed toughening interlayer of PA microparticles and interfacial bonding between PA and epoxy matrix were detected, which resulted in enhanced fracture toughness, CAI, and transverse flexural strength of the composite based on the PES/PA synergistically toughened matrix. SEM images of fracture morphology of the composite showed evidence of enhanced plastic deformation created by PES and PA, and crack deflection and bridging by PA microparticles.     

A new smart charging method for EVs for frequency control of smart grid

Nowadays, due to the shortage of fossil fuels on the one hand and their high prices on the other hand, using electric vehicles (EVs) has been increased. Charging of EVs has imposed new loads on power systems. These new and major loads have faced the frequency control and stability of power systems with new challenges. One way to deal with this new challenge is smart charging of EVs. In this method, grid condition is a key parameter that affects the charging of EV. In other words, in smart charging method, charging is performed with respect to power system parameters such as frequency. In this paper, a smart charging method based on fuzzy controller is proposed, in which charging process is performed with respect to the frequency deviation of grid and state of charge (SOC) of EV battery. To evaluate the performance of the proposed controller in control of grid frequency, IEEE 39-bus system in the presence of renewable energy sources is considered as test system. In order to the frequency analysis, this system is converted into a three-area system and, for each area, several EV categories with different numbers of EVs, battery capacity, start time of charging, and initial SOC are supposed. Moreover performance of proposed method is compared with an optimized PI controller in terms of frequency control. To investigate performance of proposed method in charging of EVs, a two area system is assumed and charging of EVs is verified by applying step loads to both areas. Simulations are carried out in MATLAB/SIMULINK environment. Results of the simulations reveal the good performance of the proposed controller in terms of frequency control of grid and charging of EVs.     

基于SCADA参量关联互信息自编码的风电机组故障检测方法

针对风力发电机组数据采集与监视控制系统(supervisory control and data acquisition,SCADA)监测参量间的耦合关联性,提出基于多参数耦合关联互信息编码的风电机组故障检测方法.该方法构建了SCADA数据的耦合关联矩阵,采用互信息变分自编码器对关联矩阵进行编码重构;将SCADA参量关联矩阵的重构误差作为机组健康评估指标,结合指数加权移动平均模型的迭代更新,对机组实时故障阈值进行自适应设置.两个风场的风电机组SCADA数据分析结果表明,所提方法充分利用了SCADA数据的耦合关联结构信息,能有效提高风电机组故障检测的准确性及对环境工况的鲁棒性.     

An online collision-free trajectory generation algorithm for human–robot collaboration

The premise of human–robot collaboration is that robots have adaptive trajectory planning strategies in hybrid work cell. The aim of this paper is to propose a new online collision avoidance trajectory planning algorithm for moderate dynamic environments to insure human safety when sharing collaborative tasks. The algorithm contains two parts: trajectory generation and local optimization. Firstly, based on empirical Dirichlet Process Gaussian Mixture Model (DPGMM) distribution learning, a neural network trajectory planner called Collaborative Waypoint Planning network (CWP-net) is proposed to generate all key waypoints required for dynamic obstacle avoidance in joint space according to environmental inputs. These points are used to generate quintic spline smooth motion trajectories with velocity and acceleration constraints. Secondly, we present an improved Stochastic Trajectory Optimization for Motion Planning (STOMP) algorithm which locally optimizes the generated trajectories of CWP-net by constraining the trajectory optimization range and direction through the DPGMM model. Simulations and real experiments from an industrial use case of human–robot collaboration in the field of aircraft assembly testing show that the proposed algorithm can smoothly adjust the nominal path online and effectively avoid collisions during the collaboration.     

A multiple test arbors-based calibration method for a hybrid machine tool

Kinematic calibration is a necessary way to guarantee the accuracy of hybrid machine tools. The traditional calibration methods have high requirements for the measuring instruments and the measurement environment, and the measurement is extremely complex. The contradiction between measurement complexity and identification accuracy is an important problem in calibration. In this paper, a multiple test arbors-based calibration method for a hybrid machine tool is presented. The tool center point (TCP) position errors of multiple test arbors are measured sequentially by virtual TCP position constraints. The error parameters can be accurately identified based on these position errors without orientation measurement. The corresponding measurement scheme is described in detail according to whether the ball diameters of the test arbors are the same. The influence of the length and number of test arbors on the calibration results is investigated, and the basic principle for the selection of test arbors is given. Finally, the proposed method is validated by simulations and experiments. The proposed method can achieve overall high-accuracy calibration with simple measurement devices and convenient measurement steps, which provides a basis for automated calibration.