2种侧围加强内框总成检具结构对比分析

概述了侧围加强内框总成与周边零件的匹配关系,从检具操作的人机工程出发,描述了侧围加强内框总成检具的2种定位方式和结构设计,对比了2种定位方式对于检测结果的影响,总结了其优点和弊端,结果表明侧围加强内框总成检具采用车内方向朝下的定位方式效果更好。     

成分配比对稀土钨电极材料组织及力学性能影响

通过固液掺杂、等静压压制、中频烧结的方法，制备了不同的氧化镧、氧化钇、氧化锆三元掺杂成分比例的钨电极材料烧结棒材，探究了不同成分配比对样品显微组织、第二相粒子分布以及宏观力学性能的影响。结果表明，氧化镧、氧化钇、氧化锆三元复合添加能够有效改善第二相粒子在钨基体中的分布形态，降低第二相在晶界的过度富集，提高钨电极材料的综合力学性能。并且当添加成分镧、钇、锆质量比为3:1:1时，材料具有最好的综合力学性能，致密度可达96.04%，显微硬度可达549.37HV0.3，抗压强度可达3785MPa，原因是此配比下第二相粒子最为细小均匀，弥散程度最高，对基体晶粒的细化作用最好，该配比下钨基体平均晶粒尺寸达到10.3μm。     

Data fusion and transfer learning empowered granular trust evaluation for Internet of Things

In the Internet of Things (IoT), a huge amount of valuable data is generated by various IoT applications. As the IoT technologies become more complex, the attack methods are more diversified and can cause serious damages. Thus, establishing a secure IoT network based on user trust evaluation to defend against security threats and ensure the reliability of data source of collected data have become urgent issues, in this paper, a Data Fusion and transfer learning empowered granular Trust Evaluation mechanism (DFTE) is proposed to address the above challenges. Specifically, to meet the granularity demands of trust evaluation, time–space empowered fine/coarse grained trust evaluation models are built utilizing deep transfer learning algorithms based on data fusion. Moreover, to prevent privacy leakage and task sabotage, a dynamic reward and punishment mechanism is developed to encourage honest users by dynamically adjusting the scale of reward or punishment and accurately evaluating users’ trusts. The extensive experiments show that: (i) the proposed DFTE achieves high accuracy of trust evaluation under different granular demands through efficient data fusion; (ii) DFTE performs excellently in participation rate and data reliability.     

Creep behaviour of an Al–Si–Al2O3 composite based on phase evolution at 1300oC

An Al–Si–Al₂O₃ composite was prepared with corundum, aluminium powder and silicon powder. A creep test was carried out at 1300°C under 0.2 MPa for 50 h in air. The results show that the Al–Si–Al₂O₃ composite performs a low constant creep rate and remain until the end of the 50-h test. This is attributed to the in-situ formation of the tough non-oxide reinforcements, whisker-like (AlN)_x(Al₂OC)_1-x solid solution and granular β-SiC, by reactions of Al and Si during creep test. The whisker-like (AlN)_x(Al₂OC)_1-x solid solution and granular β-SiC reinforcements are evenly filled in the pores, which play the role of bridging and pinning reinforcement, forming a strong network structure with corundum aggregates. Moreover, these non-oxide phases are not wetted by the liquid phases, which impel the liquid phase shrinks in the network structure in isolation during creep test. Thus, the adverse effect of the liquid phase on the high-temperature strength of the composites is eliminated, so the composites with strong network structure quickly get a stationary low-creep state. A creep mechanism model is established.     

Effects of SiC,Al2O3, and ZrO2 particles on the LBMed characteristics of Al/SiC,Al/Al2O3, and Al/ZrO2 MMCs prepared by stir casting process

The effects of SiC, Al₂O₃, and ZrO₂ particles on the characteristics of Al/SiC, Al/Al₂O₃, and Al/ZrO₂ metal matrix composites (MMCs) have been studied in the present research work. The comparison of machining characteristics has been done to analyze the behavior of various reinforced particles with the variation of laser machining variables. The output characteristics such as dross height and kerf deviation have been investigated and compared with each MMCs. SEM and XRD have been used for the investigation of morphological changes in the structure and agglomeration of reinforced particles. The crack and recast layer formation has been examined in the specimens of higher quantity of reinforced particles. It was observed that the MMC material reinforced with SiC particles has shown different behavior as compared to other MMC materials.     

When Does Communication Learning Need Hierarchical Multi-Agent Deep Reinforcement Learning

Abstract

Multi-agent systems need to communicate to coordinate a shared task. We show that a recurrent neural network (RNN) can learn a communication protocol for coordination, even if the actions to coordinate are performed steps after the communication phase. We show that a separation of tasks with different temporal scale is necessary for successful learning. We contribute a hierarchical deep reinforcement learning model for multi-agent systems that separates the communication and coordination task from the action picking through a hierarchical policy. We further on show, that a separation of concerns in communication is beneficial but not necessary. As a testbed, we propose the Dungeon Lever Game and we extend the Differentiable Inter-Agent Learning (DIAL) framework. We present and compare results from different model variations on the Dungeon Lever Game.     

多次动压影响巷道的围岩控制方法

针对厚煤层大采高工作面多次动压影响巷道围岩变形大的问题,结合成庄矿53082巷情况,分析了多次动压巷道围岩变形特征,提出锚杆索补强加固及浅部裂隙封堵和深部围岩加固的巷道加固方案,并进行井下试验。试验表明:巷道两帮最大移近量为376mm,顶底板最大移近量为502mm,巷道围岩变形控制效果较好,能够满足工作面安全生产使用要求。     

Experimental and macro finite element modeling studies on conformability behavior of woven nylon 66 composite reinforcement

Abstract

In this study, 3D hemispherical forming experiments are conducted to analyze the conformability behavior of nylon 66 plain woven reinforcements with different weft densities. To make insight into the forming process, a macro finite element model is also proposed for 3D forming of a specific woven reinforcement by using the defined ‘Fabric’ material. The results show that the proposed FEM modelling is highly correlated with experimental findings in terms of forming energy values with small and insignificant errors values, which confirm well model validity. It shows that the nylon 66 composite woven reinforcement with a lower weft density exhibits a lower forming energy (toughness) and hence a higher conformability over a hemispherical surface. On the other hand, modeling outputs clearly indicate that more wrinkling intensity appear for woven reinforcement with a higher weft density.     

基于强化学习的电力市场信息披露程度对市场成员交易行为影响研究

基于信息披露的主体框架，分析披露体系中具体的供需信息对于市场成员的引导作用。建立日前市场的双层竞价优化模型，采用强化学习的方法，通过市场中有无关键供求信息披露对于发电商报价预期的影响，推演模拟不同信息披露程度下发电商报价行为，然后从策略演化过程、策略偏离度以及策略收敛速度3个角度进行信息披露程度对市场成员交易行为影响的定量分析，验证市场供需信息的发布程度对于市场成员的引导作用，最后基于推演结果进行总结，并对我国信息披露的建设提出建议与思考。     

Effect of Processing Conditions on Flexural Strength Properties of Chicken Feather Fibre (CFF) and Its Hybrid Composites with Polypropylene Resin

ABSTRACT

This study discusses the effect of molding temperature, pressure and time on the final compression-molded hybrid composites with polypropylene resin for its flexural strength. The results of tests conducted to characterize the hybrid composites developed with different proportion of fibers and processing conditions are discussed in this study. When comparing the overall results on the flexural strength of the composites, 25:75 Chicken Feather Fiber (CFF)/Jute composite showed highest flexural strength by keeping minimum temperature, maximum pressure and medium time. The influence of pressure on flexural strength is significant compared to temperature and time. As time increases the flexural strength decreases.