Microstructural and mechanical properties assessment of transient liquid phase bonding of CoCuFeMnNi high entropy alloy

The transient liquid phase (TLP) bonding of CoCuFeMnNi high entropy alloy (HEA) was studied. The TLP bonding was performed using AWS BNi-2 interlayer at 1050 °C with the TLP bonding time of 20, 60, 180 and 240 min. The effect of bonding time on the joint microstructure was characterized by SEM and EDS. Microstructural results confirmed that complete isothermal solidification occurred approximately at 240 min of bonding time. For samples bonded at 20, 60 and 180 min, athermal solidification zone was formed in the bonding area which included Cr-rich boride and Mn₃Si intermetallic compound. For all samples, the γ solid solution was formed in the isothermal solidification zone of the bonding zone. To evaluate the effect of TLP bonding time on mechanical properties of joints, the shear strength and micro-hardness of joints were measured. The results indicated a decrement of micro-hardness in the bonding zone and an increment of micro-hardness in the adjacent zone of joints. The minimum and maximum values of shear strength were 100 and 180 MPa for joints with the bonding time of 20 and 240 min, respectively.     

高熵形状记忆合金的研究进展

高熵形状记忆合金是在等原子比NiTi合金的基础上,结合高熵合金的概念,逐渐发展起来的一种新型高温形状记忆合金。近年来,已开发出了综合性能优异的(TiZrHf)50(NiCoCu)50系和(TiZrHf)50(NiCuPd)50系高熵形状记忆合金,引起了广泛的关注和研究兴趣。本文从物相组成、微观组织、马氏体相变行为、形状记忆效应和超弹性等角度出发,综述了高熵形状记忆合金的研究进展,并对高熵形状记忆合金未来的研究重点进行了展望。     

Machine learning of phases and mechanical properties in complex concentrated alloys

The mechanical properties of complex concentrated alloys (CCAs) depend on their formed phases and corresponding microstructures.The data-driven prediction of the phase formation and associated mechanical properties is essential to discovering novel CCAs.The present work collects 557 samples of various chemical compositions,comprising 61 amorphous,167 single-phase crystalline,and 329 multi-phases crystalline CCAs.Three classification models are developed with high accuracies to category and understand the formed phases of CCAs.Also,two regression models are constructed to predict the hard-ness and ultimate tensile strength of CCAs,and the correlation coefficient of the random forest regression model is greater than 0.9 for both of two targeted properties.Furthermore,the Shapley additive expla-nation (SHAP) values are calculated,and accordingly four most important features are identified.A significant finding in the SHAP values is that there exists a critical value in each of the top four fea-tures,which provides an easy and fast assessment in the design of improved mechanical properties of CCAs.The present work demonstrates the great potential of machine learning in the design of advanced CCAs.     

Investigation the effect of FeNiCoCrMo HEA addition on properties of B4C ceramic prepared by spark plasma sintering

In this study, monolithic B₄C and B₄C-based ceramics incorporating FeNiCoCrMo dual-phase (FCC and BCC) high entropy alloys (HEAs) were produced by spark plasma sintering (SPS). The effect of additives on the densification behavior, mechanical properties, microstructures, and phase evaluation of the samples were investigated. X-ray analysis confirmed the existence of FCC structured HEA and depletion of BCC structured HEA, after high-temperature reaction between B₄C-HEAs. The addition of HEAs enhanced the densification behavior by liquid phase sintering. Furthermore, hardness and fracture toughness values of the samples increased with increasing HEAs content. Fracture toughness and hardness values for all composites were higher than the monolithic B₄C. A combination of the highest density (∼99.22 %) and the best mechanical properties (32.3 GPa hardness and 4.53 MPa m^1/2 fracture toughness) was achieved with 2.00 vol.% HEA addition.     

Entropy analysis of unsteady MHD three-dimensional flow of Williamson nanofluid over a convectively heated stretching sheet

This article addresses an investigation of the entropy analysis of Williamson nanofluid flow in the presence of gyrotactic microorganisms by considering variable viscosity and thermal conductivity over a convectively heated bidirectionally stretchable surface. Heat and mass transfer phenomena have been incorporated by taking into account the thermal radiation, heat source or sink, viscous dissipation, Brownian motion, and thermophoretic effects. The representing equations are nonlinear coupled partial differential equations and these equations are shaped into a set of ordinary differential equations via a suitable similarity transformation. The arising set of ordinary differential equations was then worked out by adopting a well-known scheme, namely the shooting method along with the Runge-Kutta-Felberge integration technique. The effects of flow and heat transfer controlling parameters on the solution variables are depicted and analyzed through the graphical presentation. The survey finds that magnifying viscosity parameter, Weissenberg number representing the non-Newtonian Williamson parameter cause to retard the velocity field in both the directions and thermal conductivity parameter causes to reduce fluid temperature. The study also recognizes that enhancing magnetic parameters and thermal conductivity parameters slow down the heat transfer rate. The entropy production of the system is estimated through the Bejan number. It is noticeable that the Bejan number is eminently dependent on the heat generation parameter, thermal radiation parameter, viscosity parameter, thermal conductivity parameter, and Biot number. The skillful accomplishment of the present heat and mass transfer system is achieved through the exteriorized choice of the pertinent parameters.     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

改进的CAGAN在虚拟试衣中的应用

针对CAGAN（Conditional Analogy GAN）换衣后效果模糊，在目标衣服与原始衣服长短不一致时效果一般，相对目标衣服保留过少的细节等问题做了相关研究并对CAGAN进行了改进，提出了新的虚拟试衣方式。经过改进的CAGAN生成一个粗糙的结果，由该结果得到目标衣服穿在模特身上改变形状后的mask，接下来利用mask对目标衣服进行变形，综合变形的衣服和第一步的结果便得到最终的试衣图像。实验结果表明，该方法解决了前面存在的问题，而且取得了非常好的效果。     

No-reference quality assessment of dynamic sports videos based on a spatiotemporal motion model

This paper proposes an approach to improve the performance of no-reference video quality assessment for sports videos with dynamic motion scenes using an efficient spatiotemporal model. In the proposed method, we divide the video sequences into video blocks and apply a 3D shearlet transform that can efficiently extract primary spatiotemporal features to capture dynamic natural motion scene statistics from the incoming video blocks. The concatenation of a deep residual bidirectional gated recurrent neural network and logistic regression is used to learn the spatiotemporal correlation more robustly and predict the perceptual quality score. In addition, conditional video block-wise constraints are incorporated into the objective function to improve quality estimation performance for the entire video. The experimental results show that the proposed method extracts spatiotemporal motion information more effectively and predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods.     

基于熵权物元模型的长三角幸福河层次评价

将幸福河概念与长江三角洲区域一体化发展战略相结合，基于自然、人类社会、人水关系3个系统构建了包含24个指标的长三角幸福河层次评价指标体系；引入需求层次理论，以基础Ⅰ层次、基础Ⅱ层次、提升Ⅰ层次、提升Ⅱ层次、幸福层次5个层次作为幸福河的层次评价等级，运用熵权物元模型，建立了长三角幸福河层次评价模型，并对2018年长三角三省一市幸福河层次等级进行了评价。结果表明，三省一市均处于转化中的中间状态，其中江苏省向提升Ⅰ层次转化，浙江省、安徽省向提升Ⅱ层次转化，上海市向幸福层次转化，说明区域整体幸福河层次处于较高水平，但稳定性较差。     

Large electrocaloric effect in two-step-SPS processed Pb(Sc0.25In0.25Nb0.25Ta0.25)O3 medium-entropy ceramics

Ferroelectric ceramic with a large electrocaloric (EC) effect at a very low electric field is very attractive in the next solid state refrigeration technology. In this work, two Pb(Sc_0.25In_0.25Nb_0.25Ta_0.25)O₃ (PSINT) medium-entropy ceramics were successfully synthesized by a spark plasma sintering (SPS) technology, including one-step-SPS processed and two-step-SPS processed samples. A large EC effect (△T ～ 0.85 K) with a high EC strength (△T/△E ～ 0.021 K cm/kV) around room temperature are obtained at a very low electric field (～40 kV/cm) in the two-step-SPS processed sample. Moreover, the working temperature range is very broad (～120 K), which can be responsible for the high relaxation degree of the dielectric peak. It can be believed that the PSINT medium-entropy ceramics can be promising candidates for application in the next-generation EC cooling devices.