Densification mechanism and microstructure characteristics of nano- and micro- crystalline alumina by high-pressure and low temperature sintering

Fully dense ceramics with retarded grain growth can be attained effectively at relatively low temperatures using a high-pressure sintering method. However, there is a paucity of in-depth research on the densification mechanism, grain growth process, grain boundary characterization, and residual stress. Using a strong, reliable die made from a carbon-fiber-reinforced carbon (C_f/C) composite for spark plasma sintering, two kinds of commercially pure α-Al₂O₃ powders, with average particle sizes of 220 nm and 3 μm, were sintered at relatively low temperatures and under high pressures of up to 200 MPa. The sintering densification temperature and the starting threshold temperature of grain growth (T_sg) were determined by the applied pressure and the surface energy relative to grain size, as they were both observed to increase with grain size and to decrease with applied pressure. Densification with limited grain coarsening occurred under an applied pressure of 200 MPa at 1050 °C for the 220 nm Al₂O₃ powder and 1400 °C for the 3 μm Al₂O₃ powder. The grain boundary energy, residual stress, and dislocation density of the ceramics sintered under high pressure and low temperature were higher than those of the samples sintered without additional pressure. Plastic deformation occurring at the contact area of the adjacent particles was proved to be the dominant mechanism for sintering under high pressure, and a mathematical model based on the plasticity mechanics and close packing of equal spheres was established. Based on the mathematical model, the predicted relative density of an Al₂O₃ compact can reach ～80 % via the plastic deformation mechanism, which fits well with experimental observations. The densification kinetics were investigated from the sintering parameters, i.e., the holding temperature, dwell time, and applied pressure. Diffusion, grain boundary sliding, and dislocation motion were assistant mechanisms in the final stage of sintering, as indicated by the stress exponent and the microstructural evolution. During the sintering of the 220 nm alumina at 1125 °C and 100 MPa, the deformation tends to increase defects and vacancies generation, both of which accelerate lattice diffusion and thus enhance grain growth.     

An ultrasonic study on ternary xPbO–(45-x)CuO–55B2O3 glasses

A series of xPbO–(45-x)CuO–55B₂O₃ glasses (5 ≤ x ≥ 40 mol %) were prepared by the melt-quenching technique. The X-ray diffraction (XRD) patterns of the prepared glasses are found to have amorphous structure. An extensive ultrasonic study has been made to explore the structural role of PbO and CuO in the borate network. Various elastic properties were calculated from the measured data of density and ultrasonic velocity. Ultrasonic velocity and elastic moduli revealed broad humps at about 20 mol % PbO, which are attributed to the borate anomaly. Below 20 mol % PbO, all Pb²⁺ ions are considered to be entering the borate network as a glass modifier. This results in the transforms the borate network from an open structure to a denser three-dimensional structure due to BO₃ → BO₄ conversion. Beyond 20 mol, addition of PbO results in the formation of metaborate, pyroborate, and orthoborate units with NBOs. This weakness the glass structure and decrease both ultrasonic velocity and elastic moduli. The elastic properties were predicted and quantitatively analyzed by taking into account the effect of boron coordination number on the compositional and structural parameters involved in Makishima–Mackenzie's theory, ring deformation model and bond compression model. An excellent agreement between the computed theoretical and experimental elastic moduli, micro-harness and Poisson's ratio was achieved for majority of samples.     

梯度铝蜂窝夹芯板的力学行为

梯度分层铝合金蜂窝板是一种有效的吸能结构,本工作在梯度铝蜂窝结构的基础上根据梯度率的概念,通过改变蜂窝芯层的胞壁长度,设计了4种质量相同、梯度率不同的铝蜂窝夹芯结构。通过准静态压缩实验,并结合非线性有限元模拟准静态及冲击态下梯度铝蜂窝夹芯结构的变形情况及其力学性能,分析对比了相同质量下梯度铝蜂窝夹芯结构在准静态下的变形模式以及冲击载荷下分层均质蜂窝结构和不同梯度率的分层梯度蜂窝结构的动态响应和能量吸收特性。结果表明:在准静态压缩过程中,铝蜂窝梯度夹芯板的变形具有明显的局部化特征,蜂窝芯的变形为低密度优先变形直至密实,层级之间的密实化应变差随芯层密度的增大而逐渐减小;在高速冲击下,梯度蜂窝板并非严格按照准静态过程中逐级变形直至密实,而是在锤头冲击惯性及芯层密度的相互作用下整体发生的线弹性变形、弹性屈曲、塑性坍塌及密实化;另外,在本工作所设计的梯度率中,当梯度率为γ₁=0.0276时,梯度蜂窝夹芯板的吸能性达到最好,相较于同等质量下的均质蜂窝夹芯板,能量吸收提高了10.63%。     

Learning shape and texture progression for young child face aging

Face aging (FA) for young faces refers to rendering the aging faces at target age for an individual, generally under 20s, which is an important topic of facial age analysis. Unlike traditional FA for adults, it is challenging to age children with one deep learning-based FA network, since there are deformations of facial shapes and variations of textural details. To alleviate the deficiency, a unified FA framework for young faces is proposed, which consists of two decoupled networks to apply aging image translation. It explicitly models transformations of geometry and appearance using two components: GD-GAN, which simulates the Geometric Deformation using Generative Adversarial Network; TV-GAN, which simulates the Textural Variations guided by the age-related saliency map. Extensive experiments demonstrate that our method has advantages over the state-of-the-art methods in terms of synthesizing visually plausible images for young faces, as well as preserving the personalized features.     

Effect of Bimodal Grain Size Distribution on the Strain Hardening Behavior of a Medium-Entropy Alloy

The evolution of strain hardening behavior of the Fe_(50)(CoCrMnNi)_(50) medium-entropy alloy as a function of the fraction of recrystallized microstructure and the grain size was studied using the Hollomon and Ludwigson equations.The specimens under study were partially recrystallized,fully recrystallized with ultrafine-grained microstructure,and fully recrystallized with coarse grains.The yield strength decreases steadily as the fraction of recry stallized micro structure and grain size increases due to the recovery process and the Hall-Petch effect.Interestingly,the bimodal grain distribution was found to have a significant impact on strain hardening during plastic deformation.For instance,the highest ultimate tensile strength was exhibited by a 0.97 μm specimen,which was observed to contain a bimodal grain distribution.Furthermore,using the Ludwigson equation,the effect of the bimodal grain distribution was established from the behavior of K_2 and n1 curves.These curves tend to show very high values in the specimens with a bimodal grain distribution compared to those that show a homogenous grain distribution.Additionally,the bimodal grain distribution contributes to the extensive L(u|")ders strain observed in the 0.97 μm specimen,which induces a significant deviation of the Hollomon equation at lower strains.     

基于GA-BP的汽车风振噪声声品质预测模型

目前对于汽车风振噪声的优化研究主要以声压级（Sound pressure level，SPL）作为单一评价指标，既不能全面反映噪声的物理属性，也无法考虑人耳对噪声的主观认知过程。为准确评价风振噪声，引入声品质，运用大涡模拟（Large eddy simulation，LES）对风振噪声进行数值仿真，根据实车道路试验判断仿真的准确性；对仿真结果进行声品质客观评价与主观评价，综合声品质客观评价参数与声品质主观评价试验结果建立BP神经网络预测模型；利用遗传算法（Genetic algorithm，GA），进一步对BP神经网络的结构参数进行优化，建立GA-BP声品质预测模型。研究结果表明，GA-BP声品质预测模型在训练速度和预测精度上都优于BP神经网络预测模型。预测模型基于声品质主客观评价结果，其预测值可以代替传统的声压级评价指标，为风振噪声提供更为准确合理的评价。     

InSAR技术在矿区地表变形监测中的应用

以禹州市梁北矿为研究区，利用2018年11月—2020年6月间35景5 m×20 m分辨率Sentinel-1A数据，采用InSAR技术，利用SBAS（短基线集InSAR）雷达干涉测量方法对梁北矿进行地面沉降信息提取解译，并通过实地调查成果认为，采用InSAR技术适合在矿区开展地表变形监测。     

钢混结构大型厂房整体式爆破拆除

采用整体式控制爆破拆除方案对华电扬州电厂钢筋混凝土框-排架结构厂房进行了爆破拆除。爆破前,对汽机房、厂房外墙和楼梯进行了预处理。将厂房划分为3个爆区,采用非电多回路网格式爆破网路,依靠东侧锅炉房倒塌施加在西侧锅炉房上的倾覆力矩,有效地克服了西侧锅炉房向西倒的难题,可为同类工程提供一定的参考。     

Gluten–starch interface characteristics and wheat dough rheology—Insights from hybrid artificial systems

Referring to the total surface existing in wheat dough, gluten–starch interfaces are a major component. However, their impact on dough rheology is largely unclear. Common viewpoints, based on starch surface modifications or reconstitution experiments, failed to show unambiguous relations of interface characteristics and dough rheology. Observing hybrid artificial dough systems with defined particle surface functionalization gives a new perspective. Since surface functionalization standardizes particle–polymer interfaces, the impact on rheology becomes clearly transferable and thus, contributes to a better understanding of gluten–starch interfaces. Based on this perspective, the effect of particle/starch surface functionality is discussed in relation to the rheological properties of natural wheat dough and modified gluten–starch systems. A competitive relation of starch and gluten for intermolecular interactions with the network-forming polymer becomes apparent during network development by adsorption phenomena. This gluten–starch adhesiveness delays the beginning of non-linearity under large deformations, thus contributing to a high deformability of dough. Consequently, starch surface functionality affects the mechanical properties, starting from network formation and ending with the thermal fixation of structure.     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).