TiAl合金表面激光重熔Al_2O_3-13wt%TiO_2复合陶瓷涂层组织结构

采用等离子喷涂和激光重熔复合工艺在TiAl合金表面制备了Al2O3-13wt%TiO2复合陶瓷涂层.为了减少重熔层裂纹等缺陷,采用较低的激光功率和能最密度进行重熔.用扫描电镜(SEM),能谱仪(EDS)和X射线衍射仪(XRD)分析了涂层形貌、微观结构和相组成.结果表明,经过激光重熔处理后,陶瓷涂层颗粒细化,片层状组织得以消失,致密性提高,获得了基本没有裂纹等缺陷的重熔层;激光重熔亚稳相γ-Al2O3转变为稳定相α-Al2O3;由于陶瓷材料导热系数较低的影响,激光重熔时无法使整个陶瓷层实现重熔,重熔后的陶瓷涂层形成了晶粒细小的等轴品重熔区、烧结区以及片层状残余等离子喷涂区.     

固溶对Al-Cu-Li-X合金组织和性能的影响

通过OM,SEM,TEM和力学性能测试等手段,研究了固溶处理对Al-Cu-Li-X合金组织和性能的影响.结果表明:2A97合金T6态(未拉伸态)在520℃固溶80min,淬火转移时间小于15s,水淬,获得了合适力学性能,抗拉强度为493MPa,屈服强度为415MPa,延伸率为7.3%.合金时效强化相以T1,θ′/θ″和δ′ 相为主.经510℃,520℃和530℃固溶水淬,再165℃时效18h,随固溶温度升高,强度和塑性趋于减小.在520℃固溶水淬,再165℃时效18h,随固溶时间延长,屈服强度趋于升高,延伸率先升后降低.随固溶温度升高和时间延长,可溶过剩相数量和体积分数减小.520℃固溶后时效,基体沉淀相T1,θ′/θ″和δ′ 相数量多,尺寸小,分布均匀.     

沉积温度对TiB_2涂层的组成与形貌的影响

利用TiCl4-BCl3 -H2-Ar反应体系,用化学气相沉积法(CVD)在石墨基体上沉积了TiB2涂层,并对涂层的物相、沉积速率、微观结构、表面形貌进行了分析.结果表明:沉积的涂层物相由TiB2组成.随着沉积温度的提高,沉积速率加快,涂层的显微硬度先增加后降低,950℃时达到最大值.沉积温度的升高,TiB2涂层颗粒尺寸明显增大,在900℃～950℃范围内能沉积出结构致密、颗粒尺寸适合的TiB2涂层.分析了TiB2涂层的沉积机理.     

一种P2P MMOG中协调者节点的信誉管理模型

提出一种适合P2P MMOG中的协调者节点的信誉管理模型.该模型针对玩家承担协调者节点任务时的信誉进行评估,采用模糊理论计算玩家信誉,从数据包延迟和玩家感受两个方面对协调者节点的信誉进行评估,并给出相应的信誉评估算法.实验结果表明,该模型可以有效评估协调者节点的信誉情况,为协调者选举提供很好的信誉支持.     

变形高温合金中钛含量测定方法的研究

运用N2O-C2H2火焰原子吸收光谱法测定变形高温合金中的钛含量。考察了钛的最佳测定条件以及线性浓度范围。在样品测定中对干扰因素进行了综合考虑。实验表明：该方法具有干扰小、选择性好,操作简便、容易掌握、分析周期短等优点;测定样品钛含量在10-60 mg/L时,其相对标准偏差均小于1.0%（n=10）;标准加入回收率均为97.2%-98.3%（n=6）;该分析方法适用于变形高温合金中钛含量的测定。     

压力对有序LixTiS2电子性质的影响

采用基于密度泛函理论的局域密度近似（LDA）,计算了压力对LixTiS2（x=0.0．25,0．5,0．75,1）的晶格结构和电子结构的影响。研究发现,对于同一“含量的LixTiS2,随着压力的增大,LixTiS2的晶格参数有规律的减小,插层化合物中Li的含量越高,材料越难被压缩;在压强等于1GPa左右时各体系的总能量达到最小值。各体系的费米能级随着压力的增大而降低;各原子的电子能态密度产生了漂移,压力还导致了S原子和．Ti原子p-d杂化减弱。“原子和S原子的共价相互作用得到了增强     

Sol-gel法制备CuSO4,Cu(AC)2掺杂SnO2的气敏性能研究

以溶胶-凝胶法(Sol-gel)制备纳米SnO2及其不同比例的铜源掺杂物.采用热分析(TG-DTA),XRD等手段对其产物进行表征,利用静态配气法测试了掺杂产物的气敏性能.实验结果表明:采用Sol-gel法制备掺杂SnO2的气敏性能,同时受铜源和掺杂量的影响,且掺杂产物对乙醇不很敏感,但在150℃工作温度下对0.005%的H2S具有36367倍的高灵敏度.     

对等网络平台搜索模型

分析了无结构对等网络若干经典搜索算法的优缺点,进而提出一种兼顾查询深度与广度的平台搜索算法(PFSA:Platform Searching Algorithm).该算法在提高了网络资源利用率、保持较快搜索速度的同时维持着较低的消息冗余度;其子查询算法针对Blind Counter Rumor Mongering算法的不足加以改进,使得搜索算法可以根据不同的网络动态地调整自身参数,大大提高了算法的鲁棒性.     

High Temperature Flow Stress Prediction of Nano-Al_2O_3/Cu Composite Using an Artificial Neural Network

Alumina dispersion strengthened copper composite (nano-Al2O3/Cu composite) was recently emerged as a kind of potentially vi-able and attractive engineering material for applications requiring high strength, high thermal and electrical conductivities and resistance to softening at elevated temperatures. The nano-Al2O3/Cu composite was produced by internal oxidation. The microstructures of the composite were analyzed by the TEM and its hot deformation behavior was investigated by means of continuous compression tests per-formed on a Gleeble 1500 thermo-simulator. Making use of the modified algorithm–Levenberg-Marquardt (L-M) algorithm BP neural network, a model for predicting the flow stresses during hot deformation was set up on the base of the experimental data. Results show that the microstructures of the composite are characterized by uniform distribution of nano-Al2O3 particles in Cu-matrix. The sliding of dislocations is the main deformation mechanism. The dynamic recovery is the main softening mode with the flow stress decreasing gen-tly from 500 ℃ to 850 ℃. The recrystallization of Cu-matrix can be retarded late into as high as 850 ℃, when it happens only partially. The well-trained BP neural network model can accurately describe the influence of the temperature, strain rate, and true strain on the flow stresses, therefore, it can precisely predict the flow stresses of the composite under given deforming conditions and provide a new way to optimize hot deforming process parameters.     

Thermoelectric Properties of Mg_2Si_(1-x)Sn_x Synthesized by Bulk Mechanical Alloying

Bulk mechanical alloying (BMA) has been successfully applied to solid-state synthesis of p-type and n-type thermoelectric mate-rials Mg2Si1–xSnx (x = 0, 0.2, 0.4, 0.6, 0.8, 1) from element-powders at the room temperature in a relatively short time. The electrical conductivity, the Seebeck coefficient and the thermal conductivity of the Mg2Si1–xSnx are quite sensitive to the x-content. With the x-content rising, the electrical conductivity increases. When x = 0.6, it reaches the lowest and Mg2Si shows an n-type of semi-conducting. However, when x = 0.2 and T >525 K, the Seebeck coefficient of the samples will change the opposite way. While x≥0.4, the samples present a p-type of semi-conducting. The figure of merit, Z of Mg2Si1–xSnx will be obtained in the range from 300 K to 700 K. When x = 0.6, Z proves to be higher than that of other samples at 300 K≤T≤650 K.