聚酯帘线的发展趋势

从聚酯的结构设计、帘线浸胶工艺和在轮胎中的应用3个方面阐述聚酯结构特点与产品性能的关系。聚酯帘线行业应该密切关注汽车行业的发展态势,把握市场先机,进一步发展壮大;在新能源汽车逆势增长的形势下,高性能聚酯帘线需求量较大,帘线企业应拓展高性能聚酯帘线在轮胎冠带层、带束层和胎体等部件中的应用,同时联合轮胎企业开发特种聚酯材料和混纺材料等新产品。     

Stress distribution in thick-walled cylinder due to non-uniform radial pressure on the example of reinforcement corrosion in concrete

This paper presents an analytical solution to the non-uniform pressure on thick-walled cylinder. The formulation is based on the linear elasticity theory (plain strain) and stress function method. As an example, the proposed solution is used to model the stress distribution due to non-uniform steel reinforcement corrosion in concrete. The model is formulated considering different scenarios of corrosion pressure distribution. It is validated against the finite element model for different cases of non-uniform pressure distributions. The results show that the corrosion-induced cracks are likely to start just beyond the anodic zone. This is confirmed by the experimental tests on concrete cylinder exposed to non-uniform accelerated corrosion of steel reinforcement. The model can be effectively used to calculate the distribution of corrosion-induced stresses in concrete.     

基于神经网络和遗传算法的锭子弹性管性能优化

为得到减振弹性管对下锭胆的支承弹性和锭子高速运动下的稳定性等性能的最优匹配效率,依据减振弹性管的等效抗弯刚度及底部等效刚度系数公式,利用MatLab数值分析软件构建弹性管抗弯刚度和底部挠度数学模型。首先,结合Isight优化软件基于径向基神经网络构建其近似模型,且使精度达到可接受水平,并以模型的关键结构参数弹性模量、螺距、槽宽、壁厚为设计变量,结合遗传算法对弹性管抗弯刚度和底部挠度进行多目标优化设计,得到Pareto最优解集和Pareto前沿图,确定出减振弹性管结构工艺参数的优化方案。通过对优化数据进行分析发现,该方案在保证减振弹性管弹性的同时,其底部振幅明显减弱。     

3＋9×0.175NT钢丝帘线在轻型载重子午线轮胎胎体中的应用

研究3+9×0.175NT钢丝帘线在轻型载重子午线轮胎胎体中的应用。结果表明,采用1层压延密度为83根·dm-1的3+9×0.175NT钢丝帘布替代3层压延密度为110根·dm^-1的1670dtex/2-28EPI聚酯帘布用于6.50R1610PR轻型载重子午线轮胎胎体,压延工艺良好,成型和硫化过程正常。成品轮胎质量减小,成本降低,充气外缘尺寸、强度性能、耐久性能和高速性能均符合国家标准或企业标准要求。     

Evaluation of standard and real fire exposures on thermal response of rail car floor assembly

This work developed a computational methodology to evaluate and compare standard fire exposures such as those outlined in ASTM E119 with real fire exposures and determine the difference in the temperature rise of a rail car floor assembly. The real fire exposures simulated in this work were identified in a review of incidents and consisted of a constantly-fed diesel fuel spill, a localized trash fire, and a gasoline spill simulated from a collision of the railcar with an automobile. These realistic fire exposures were applied to a variety of exemplar rail cars representative of single-level and bi-level passenger cars. These floor assembly models exposed to realistic fires were simulated in Fire Dynamics Simulator (FDS). The thermal exposure at the underside of railcar provided by FDS was coupled with a thermal model in ABAQUS, which provided the evolution of temperature in different components of the floor assembly. The standard scenarios were simulated for 2 hours instead of the typical 30 minutes to identify the appropriate exposure duration in ASTM E119, which can better represent a real fire scenario. The average and maximum temperatures predicted at the unexposed surface for both scenarios were compared with the threshold values given in NFPA 130.     

浅析智能网联汽车感知系统电磁兼容性

本文将就智能网联汽车感知系统中的电子兼容技术进行深入的分析和探究。     

“都林”机器人内板连续式喷涂技术应用浅析

结合新工厂的应用经验,通过对"都林"机器人的喷涂方式、布局、雾化器配置选型、参数设置、仿形设计及品质控制要点等方面的分析,总结了连续式喷涂的优势及技术控制要求。     

Prediction and sensitivity analysis of CNTs-modified asphalt’s adhesion force using a radial basis neural network model

Abstract

The expected longer service life of modified asphalt can be jeopardized by different environmental factors, such as moisture, oxidation, etc. which affect the desired properties by altering the adhesive property. An insight into knowledge of the adhesive property of the asphalt can help in providing more durable asphalt pavement. The study attempted to develop different models of adhesive properties of polymers and carbon nanotubes (CNTs) modified asphalt binders. The polymer-CNT modified asphalt is processed to prepare different types of samples, by simulating the damage due to moisture and oxidization, following the corresponding standard method. An Atomic Force Microscopy (AFM) was employed to assess the nanoscale adhesion force of the tested samples following the existing functional group in asphalt. Finally, the study has developed Radial Basis Function Neural Network (RBFNN) as a function of different parameters including; asphalt chemistry (i.e. AFM tip type and constant), type and percentages of polymers and CNTs and different environmental exposures (oxidation, moisture, etc.) to predict the nano adhesion force of asphalt. It is observed that the adhesive property of the Styrene–Butadiene modified asphalt is more consistent compared to the Styrene–Butadiene–Styrene modified asphalt, while the presence of Single-Wall Nanotubes (SWNT) is observed to affect the adhesive properties of asphalt significantly as compared to Multi-Wall Nanotubes (MWNT). The higher accuracy level of RBFNN model also indicates that the functional group (tip-type) adding with the percentages and types of polymers and CNTs significantly affect the adhesive properties of asphalt.     

Molecular Dynamics Study of Radial Corrugation in Carbon Nanotubes

Molecular dynamics simulations of multiwalled carbon nanotubes under hydrostatic pressure are performed to elucidate the novel class of radial buckling in the systems. It is revealed by all-atom simulations that the initial circular cross section transforms into a flower-like wavy configuration at critical pressure on the order of hundreds mega pascals or less. This kind of radial buckling, called radial corrugation, originates from the competition of the three relevant energies in the system: in-plane strain energy, van der Waals interaction energy between adjacent tubes, and out-of-plane bending energy. Their possible consequences for physical properties of carbon nanotubes are also discussed.     

Deep reinforcement learning for a color-batching resequencing problem

In automotive paint shops, changes of colors between consecutive production orders cause costs for cleaning the painting robots. It is a significant task to re-sequence orders and group orders with identical color as a color batch to minimize the color changeover costs. In this paper, a Color-batching Resequencing Problem (CRP) with mix bank buffer systems is considered. We propose a Color-Histogram (CH) model to describe the CRP as a Markov decision process and a Deep Q-Network (DQN) algorithm to solve the CRP integrated with the virtual car resequencing technique. The CH model significantly reduces the number of possible actions of the DQN agent, so that the DQN algorithm can be applied to the CRP at a practical scale. A DQN agent is trained in a deep reinforcement learning environment to minimize the costs of color changeovers for the CRP. Two experiments with different assumptions on the order attribute distributions and cost metrics were conducted and evaluated. Experimental results show that the proposed approach outperformed conventional algorithms under both conditions. The proposed agent can run in real time on a regular personal computer with a GPU. Hence, the proposed approach can be readily applied in the production control of automotive paint shops to resolve order-resequencing problems.