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.     

Topological homogenization of metamaterial variability

With the proliferation of additive manufacturing and 3D printing technologies, a broader palette of material properties can be elicited from cellular solids, also known as metamaterials, architected foams, programmable materials, or lattice structures. Metamaterials are designed and optimized under the assumption of perfect geometry and a homogeneous underlying base material. Yet in practice real lattices contain thousands or even millions of complex features, each with imperfections in shape and material constituency. While the role of these defects on the mean properties of metamaterials has been well studied, little attention has been paid to the stochastic properties of metamaterials, a crucial next step for high reliability aerospace or biomedical applications. In this work we show that it is precisely the large quantity of features that serves to homogenize the heterogeneities of the individual features, thereby reducing the variability of the collective structure and achieving effective properties that can be even more consistent than the monolithic base material. In this first statistical study of additive lattice variability, a total of 239 strut-based lattices were mechanically tested for two pedagogical lattice topologies (body centered cubic and face centered cubic) at three different relative densities. The variability in yield strength and modulus was observed to exponentially decrease with feature count (to the power −0.5), a scaling trend that we show can be predicted using an analytic model or a finite element beam model. The latter provides an efficient pathway to extend the current concepts to arbitrary/complex geometries and loading scenarios. These results not only illustrate the homogenizing benefit of lattices, but also provide governing design principles that can be used to mitigate manufacturing inconsistencies via topological design.     

Application of artificial neural network method to predict the breakage properties of PGE bearing chromite ore

In the present investigation, systematic grinding experiments were conducted in a laboratory ball mill to determine the breakage properties of low-grade PGE bearing chromite ore. The population balance modeling technique was used to study the breakage parameters such as primary breakage distribution (B_{i, j}) and the specific rates of breakage (S_i). The breakage and selection function values were determined for six feed sizes. The results stated that the breakage follows the first-order grinding kinetics for all the feed sizes. It was observed that the coarser feed sizes exhibit higher selection function values than the finer feed size. Further, an artificial neural network was used to predict breakage characteristics of low-grade PGE bearing chromite ore. The predicted results obtained from the neural network modeling were close to the experimental results with a correlation of determination R² = 0.99 for both product size and selection function.     

Numerical study on methane/air combustion characteristics in a heat-recirculating micro combustor embedded with porous media

Micro-combustor is a portable power device that can provide energy efficiently, heat recirculating is considered to be an important factor affecting the combustion process. For enhancing the heat recirculating and improving the combustion stability, we proposed a heat-recirculating micro-combustor embedded with porous media, and the numerical simulation was carried out by CFD software. In this paper, the effect of porous media materials, thickness and inlet conditions (equivalence ratio, inlet velocity) on the temperature distribution and exhaust species in the micro combustor are investigated. The results showed that compared with the micro combustor without embedded porous media (MCNPM), micro-combustor embedded with porous media (MCEPM) can improve the temperature uniformity distribution in the radial direction and strengthen the preheating capacity. However, it is found that the embedding thickness of porous media should be reasonably arranged. Setting the thickness of porous media to 15 mm, the combustor can obtain excellent comprehensive capacity of steady combustion and heat recirculating. Compared the thermal performance of Al₂O₃, SiC, and ZrO₂ porous media materials, indicating that SiC due to its strong thermal conductivity, its combustion stabilization and heat recirculating capacity are obviously better than that of Al₂O₃ and ZrO₂. With the porous media embedded in the micro combustor, the combustion has a tempering limit of more than 10 m/s, and the flame is blown out of the porous media area over 100 m/s. The reasonable equivalence ratio of CH₄/air combustion should be controlled within the range of 0.1–0.5, and “super-enthalpy combustion” can be realized.     

2020年电子电路技术热点

文章归纳了2020年电子电路产业一些技术热点,主要有5G电路板设计和基材,制造方面半加成法、3D打印、直接金属化孔电镀和垂直互连结构等技术,以及集成电路封装载板技术。     

Influence of lanthanum on enhancement of mechanical and electrical properties of Cu-Al_2O_3 composites

Two kinds of Cu-Al_2O_3 composites(with and without La) were prepared via mechanical alloying-spark plasma sintering(MA-SPS) method. Microstructure, mechanical properties and electrical resistivity were investigated systematically using metallography, scanning electron microscopy, transmission electron microscopy, mechanical and electrical properties testing. The results indicate that an appropriate amount of La can homogenize the distribution of Al_2O_3. As such, yield strength, ultimate tensile strength and elongation of Cu-Al_2O_3-La are greatly increased. Some semi-coherent interface between Cu and Al_2O_3 is found, which means a low interface energy. The grain shape of Cu changes to irregular band with the addition of La. This change results in a density decrease of grain boundary and reduces electrical resistance. Lanthanum may exist in the form of La_2O_3.     

The effect of TiO2 nanotube morphological engineering and ZnS quantum dots on the water splitting reaction: A theoretical and experimental study

Ordered arrays of TiO₂ nanotubes with smooth and rippled morphologies were prepared by one-step titanium oxidation in NH₄F and ethylene glycol solution. The samples were then decorated with ZnS using a microwave-assisted solvothermal method. The experiments under constant or pulsed applied voltage resulted in smooth and rippled TiO₂material morphologies, respectively. Field emission scanning electron microscopy, incident photon-to-current efficiency, linear sweep voltammetry and electrochemical impedance spectroscopy were used to investigate the structure and morphology of the TiO₂ nanotubes, along with their photoelectrochemical activity in the water splitting reaction. An envelope function was proposed to correlate the anisotropic morphologies and broad distribution of mobility due to the random nature of charge carrier transport. The smooth and rippled morphologies were evaluated using the transmission line model. First-principles quantum mechanical calculations based on the density functional theory at the B3LYP level are conducted to obtain a better understanding of optical properties of TiO₂.     

Effects of deformation rate on properties of Nd,Y-codoped CaF2 transparent ceramics

Different deformation rates of Nd,Y-codoped CaF₂ transparent ceramics were prepared by ceramization of single crystals. The deformation rate effects on the crystallization behaviors, microstructures, mechanical properties, and optical performances were investigated for the first time. The results indicate that the comprehensive performances of Nd,Y-codoped CaF₂ ceramic (△a?=?62%) are the most optimal compared with other ceramics having different deformation rates (△a?=?34%, 40%, 50%, and 75%). In further investigations of the optical properties, the Nd,Y-codoped CaF₂ ceramic (△a?=?62%) sample exhibited a high transparency (T_a?>?91%, 3-mm thick，250?～?1200?nm), low light scattering, superior fracture toughness (K_1c?～?0.71?MPa·m^1/2), strong fluorescence emission, long lifetime (τ?=?348.72?μs), and broad FWHM (29.2?nm), promising a good candidate for high-power laser material.     

Grain boundary segregation and its influences on ionic conduction properties of scandia doped zirconia electrolytes

Solid oxide fuel cell is a promising energy conversion system which converts chemical energy into electrical energy directly. Electrolyte is the key component and determines the working temperature. In this paper,ceria and scandia co-doped zirconia electrolytes sintered from 1300 to 1550 ℃ were chosen as research objects. Scanning electron microscopy, X-ray diffraction and transmission electron microscopy were performed to characterize the ceramic samples. The effects of grain size and grain boundary element segregation on the electrical conductivity were focused. Electrochemical impedance spectroscopy was used to calculate the bulk, grain boundary and specific grain boundary conductivity. Results show that the bulk and grain boundary ionic conductivity increases with the increasing grain size.However, the specific grain boundary conductivity decreases with the increasing grain size. This is explained by the fact that Sc~(3+) is segregated at the grain boundary, which leads to higher oxygen vacancy concentration when sintered at lower temperature.     

Hydroxyapatite/bioactive glass functionally graded materials (FGM) for bone tissue engineering

In this work, HA/bioactive glass Functionally Graded Materials (FGMs) are obtained for the first time by means of Spark Plasma Sintering (SPS). Two series of highly dense 5 layered products, namely FGMS1 and FGMS2, are prepared under optimized SPS conditions, i.e. 1000 °C/2 min/16 MPa and 800 °C/2 min/50 MPa, respectively, using a die with varying cross section.Results arising from XRD, SEM, mechanical and biological characterization in SBF, evidence that lower temperature and higher-pressure levels used for FGMS2 samples provide better materials in terms of microstructure, compactness, hardness, elastic modulus and in vitro bioactivity. Indeed, a fully sintered and crack-free microstructure with no crystallisation at the top layer (100% bioactive glass) is correspondingly produced.The obtainment of such FGMs is quite promising, since it permits to vary the relative volume fractions of the two constituents and, consequently, tailor the biological response for specific clinical applications.