Study on Steel-Mushy Al-20Sn Alloy Bonding

Steel-mushy Al-20Sn alloy bonding was studied for the first time. The relationship model about preheat temperature of steel plate, solid fraction of Al-20Sn alloy mushy, rolling speed and interfacial shear strength of bonding plate could be established by artificial neural networks perfectly. This model could be optimized with a genetic algorithm. The optimum bonding parameters were: 505℃ for preheat temperature of steel plate, 34.3% for solid fraction of Al-20Sn alloy mushy and 10 mm/s for rolling speed, and the largest interfacial shear strength of bonding plate was 71.2 MPa.     

Influence of Rolling Treatment on Interfacial Shear Strength of Steel-mushy Al-7graphite Bonding Plate

At room temperature, the rolling treatment of steel-mushy Al-7graphite bonding plate was carried out under different relative reduction. The influence of rolling on interfacial mechanical property of this bonding plate was studied. The results show that, for steel-mushy Al-7graphite bonding plate which is made up of 1.2 mm in thickness 08AI steel plate and 2.0 mm in thickness Al-7graphite layer, there is a nonlinear relationship between interfacial shear strength of bonding plate and relative reduction of rolling. When relative reduction of rolling is smaller than 2.59%, with the increasing of relative reduction, interfacial shear strength of bonding plate increases gradually. When relative reduction of rolling is bigger than 2.59%, with the increasing of relative reduction, interfacial shear strength of bonding plate decreases continuously. When relative reduction of rolling is 2.59%, the largest interfacial shear strength 77.0 MPa can be obtained.     

Influence of Diffusion Time on Steel—Aluminum Solid to Liquid Bonding Interfacial Structure

The bonding of steel plate to aluminum liquid was conducted using rapid solidification. The influence of diffusion time on interfacial structure was studied. The results showed that under the condition of 750(C) for the temperature of aluminum liquid and 200(C) for the preheat temperature of steel plate, when diffusion time was shorter than 4.3 s, there was only Fe-AI solid solution at the interface. When diffusion time was longer than 4.3 s, Fe-AI compound began to form at the interface. The relationships between diffusion time t and thickness of Fe-AI compound layer H are H=-9.72+2.62t-0.08t2 (4.3 s15 s).     

Influence of Diffusion Time on Steel-Aluminum Solid to Liquid Bonding Interracial Structure

The bonding of steel plate to aluminum liquid was conducted using rapid solidification. The influence of diffusiontime on interfacial structure was studied. The results showed that under the condition of 750℃ for the temperatureof aluminum liquid and 200℃ for the preheat temperature of steel plate, when diffusion time was shorter than 4.3 s,there was only Fe-Al solid solution at the interface. When diffusion time was longer than 4.3 s, Fe-Al compoundbegan to form at the interface. The relationships between diffusion time t and thickness of Fe-Al compound layer Hare H＝-9.72+2.62t-0.0St2 (4.3 s≤t≤15 s) and H＝2.T9+0.647t-0.033t2 (t＞15 s).     

Semi-solid Pressing Bonding Strength between Steel and Cu-graphite Composite

The pressing bonding of steel plate with QTi3.5-3.5graphite slurry was studied. The relationship among preheating temperature of steel plate, preheating temperature of dies, solid fraction of QTi3.5-3.5graphite slurry, and interfacial shear strength of bonding plate could be established with artificial neural networks perfectly. This model could be optimized with a genetic algorithm. The results show that the optimum bonding parameters are: 618℃ for preheating temperature of steel plate, 526℃ for preheating temperature of dies and 46.2% for solid fraction of QTi3.5-3.5graphite slurry, and the largest interfacial shear strength of bonding plate is 128.3 MPa.     

热浸镀Al－2％Si钢带界面化合物层的组成和结构

本文利用俄歇电子谱（ＡＥＳ）及透射电镜（ＴＥＭ）对比分析了国产、日本、和西德热浸镀Ａｌ－２％Ｓｉ钢带界面化合物层的厚度、结构和组成。结果表明，Ｓｉ以Ｆｅ＿２（ＡｌＳｉ）＿５的形式存在于Ｆｅ＿２Ａｌ＿５结构的界面化合物中。与热浸镀Ａｌ相比，添加Ｓｉ后导致界面化合物层厚度下降的原因在于Ｓｉ填充了Ｆｅ＿２Ａｌ＿５结构中原子空位、阻碍了Ａｌ原子的扩散。     

固体电解质(玻璃)与硅的阳极连接机理及界面分析

固体电解质(玻璃)与硅片通过阳极连接可以实现良好键合.采用SEM和EDS对界面结构进行了分析.连接的过程与由耗尽层产生的静电场力有紧密关系,通过建立的模型分析了电场力产生的原因.实验表明:温度、压力、连接时间、表面光洁度、电压是影响连接质量的重要因素,优化连接参数是形成良好连接界面的前提.     

Molecular Dynamics Study on Interfacial Energy and Atomic Structure of Ag/Ni and Cu/Ni Heterophase System

The results of molecular dynamics calculations on the interfacial energies and atomic structures of Ag/Ni and Cu/Ni interfaces are presented. Calculation on Ag/Ni interfaces with low-index planes shows that those containing the (111) plane have the lowest energies, which is in agreement with the experiments. Comparing surface energy with interfacial energy, it is found the order of the interfacial energies of Ag/Ni and Cu/Ni containing the planes fall in the same order as solid-vapor surface energies of Ag, Cu and Ni. In this MD simulation, the relaxed atomic structure and dislocation network of (110)Ag||(110)Ni interface are coincident to HREM observations.     

Superplastic Forming and Diffusion Bonding for Sandwich Structure of Ti-6Al-4V Alloy

Superplastic forming and diffusion bonding (SPF/DB) is a well-established process for the manufacture of components almost exclusively from Ti-6AI-4V sheet material. The sandwich structure of Ti-6AI-4V alloy is investigated. The effects of the microstructure on the SPF/DB process were discussed. The microstructure at the interfaces and the distribution of thickness were researched.     

聚合物纳米复合材料界面结构的表征

聚合物中加入纳米粒子可以制得性能更加优异的纳米复合材料,其中纳米粒子和聚合物基体间的界面结构对纳米复合材料的性能起着重要作用.综述了近些年来聚合物纳米复合材料中界面结构的表征手段,如红外光谱(FTIR)、核磁共振技术(NMR)、热重(TGA)、电子显微镜、原子力显微镜(AFM)、X射线光电子能谱(XPS)、小角中子散射(SANS)及小角X射线散射(SAXS)等,并对这一领域的研究进行了展望.     

Preparation and Interfacial Stability of SiCf/Ti-6Al-4V Composites Fabricated Using Powder Coated Fiber Method

1. IntroductionContinuous fiber-reinforced disordered titanium alloys and ordered titanium alloy matrix composites havreceived considerable attention as advanced aerospacstructural materials[1,2]. They possess unique combinations of several attractive properties such as high specifistiffness, high specific strength, excellent corrosion resistance, and good thermal stability etc. These novel materials can be applied in gas turbine engines, airframefor hypersonic aircraft, and advanced energy co…     

基于BP人工神经网络的混凝土强度预测

混凝土强度是结构设计中控制的主要指标,其数值决定于水灰比、胶凝材料用量、矿物掺量、外加剂用量等多种因素,常规计算混凝土强度的公式因个人理解的不同而各异,一种仿生模型—人工神经网络则能很好地解决这个难题,文中尝试用人工神经网络对不同混凝土强度进行预测,结果表明此模型的可靠度很高,可以用以优化混凝土的试配,节约大量的时间、人力、物力和财力.     

Prediction of fracture parameters of concrete by Artificial Neural Networks

Modelling of material behaviour generally involves the development of a mathematical model derived from observations and experimental data. An alternative way discussed in this paper is Artificial Neural Network (ANN)-based modelling which is a subfield of artificial intelligence. The main benefit in using an ANN approach is that the network is built directly from experimental data using the self-organising capabilities of the ANN. In this paper the Two-Parameter Model (TPM) in the fracture of cementitious materials is modelled with a back-propagation ANN. The results of an ANN-based TPM look viable and very promising.     

Adversarial Attacks on License Plate Recognition Systems

The license plate recognition system (LPRS) has been widely adopted in daily life due to its efficiency and high accuracy. Deep neural networks are commonly used in the LPRS to improve the recognition accuracy. However, researchers have found that deep neural networks have their own security problems that may lead to unexpected results. Specifically, they can be easily attacked by the adversarial examples that are generated by adding small perturbations to the original images, resulting in incorrect license plate recognition. There are some classic methods to generate adversarial examples, but they cannot be adopted on LPRS directly. In this paper, we modify some classic methods to generate adversarial examples that could mislead the LPRS. We conduct extensive evaluations on the HyperLPR system and the results show that the system could be easily attacked by such adversarial examples. In addition, we show that the generated images could also attack the black-box systems; we show some examples that the Baidu LPR system also makes incorrect recognitions. We hope this paper could help improve the LPRS by realizing the existence of such adversarial attacks.     

Neural and fuzzy model performance evaluation of a dynamic production system

Although mathematical modelling techniques are very well developed, some production processes are difficult to be modelled by these modelling techniques or their math-models are too complex to be used for real-time control due to uncertain, imprecise and vague parameters’ relations. Spray dryers are complex, dynamic and ill-defined production processes. Their product (powder) must have a controllable size distribution consisting of spherical shapes and free-flowing characteristic of particles, which is required for an ideal pressing operation to overcome the product sticking in the dies. The relations of production process' parameters are highly non-linear. In this study, these non-linear parameters were studied and three different soft-computing intelligent models were developed and used to predict uncertain parameter relations. The first is the fuzzy model of the production process; the others are the artificial neural network (ANN) architectures; the back-propagation multilayer perceptron (BPMLP) algorithm and the radial basis function network (RBF). To deal with uncertainty and vagueness of the production system, a method (methodology) based on a fuzzy hierarchical analytic process modelling approach and two ANN approaches was applied. The performance of the BPMLP algorithm was found most vigorous than the RBF and fuzzy modelling approach.     

Reliability analysis of laminated composite structures using finite elements and neural networks

Saving of computer processing time on the reliability analysis of laminated composite structures using artificial neural networks is the main objective of this work. This subject is particularly important when the reliability index is a constraint in the optimization of structural performance, because the task of looking for an optimum structural design demands also a very high processing time. Reliability methods, such as Standard Monte Carlo (SMC), Monte Carlo with Importance Sampling (MC–IS), First Order Reliability Method (FORM) and FORM with Multiple Check Points (FORM–MCPs) are used to compare the solution and the processing time when the Finite Element Method (FEM) is employed and when the finite element analysis (FEA) is substituted by trained artificial neural networks (ANNs). Two ANN are used here: the Multilayer Perceptron Network (MPN) and the Radial Basis Network (RBN). Several examples are presented, including a shell with geometrically non-linear behavior, which shows the advantages using this methodology.     

RTM成型复合材料界面及其改性的研究

界面的好坏直接影响RTM成型复合材料的力学性能。本文研究了RTM成型复合材料不同层次界面的特点，形成机理，并采用冷等离子处理对RTM成型复合材料界面性能进行改性。结果表明，RTM成型复合材料不同层次界面性能是不同的，通过冷等离子体处理可以减少不同层次界面之间的差别，进而可以改善复合材料的力学性能。     

Structure of point defects in B2 Fe-Al alloys: An atomistic study by semi-empirical simulation

We present the first results of a simulation study of point-defect properties in B2 ordered Fe-Al alloys (34% < x_Al < 52%). After examining the T = 0 K energetics of simple defects, we turn to complexes and discuss the usual hypothesis of independent elementary defects. Almost all complex defects involving an Al vacancy and an Al antisite atom in nearest neighbour position are shown to be unstable, confirming that Al vacancies are rare in these alloys. Small deviations from stoichiometry induce a change in the nature of defects: whereas on the Al-rich side, isolated Al antisite atoms dominate, Fe antisite atoms have a strong trend towards clustering on the Fe-rich side, leading to the formation of local DO₃ order.     

Optimization of Machining Characteristics for Al／SiCp Composites using ANN／GA

The present work is focused on optimization of machining characteristics of Al/SiCp composites.The machining characteristics such as specific energy,tool wear and surface roughness were studied.The parameters such as volume fraction of SiC,cutting speed and feed rate were considered.Artificial neural networks(NN) was used to train and simulate the experimental data.Genetic algorithms(GA) was interfaced with ANN to optimize the machining conditions for the desired machining characteristics .Validation of optimized results was also performed by confirmation experiments.     

Novel artificial neural network model for evaluating hardness of stir zone of submerge friction stir processed Al 6061-T6 plate

Abstract

The Al alloy 6061-T6 was friction stir processed at submerged condition and different tool rotation speeds ω and processing speeds V. The effect of processing parameters on hardness of stir zone was investigated. In order to derive out the relationship between the hardness of stir zone and processing parameters and optimising them, some tests were carried out and a matrix of variation parameters of process was filled and used for training of an artificial neural network (ANN) model. A sensitivity analysis was carried out using the ANN model. It is shown that, among the two process parameters, the processing speed V is more important on stir hardness. In addition, a safe zone can be defined by ANN model in which superior hardness can be achieved.