Machine-learning-assisted prediction of the mechanical properties of Cu–Al alloy

The machine-learning approach was investigated to predict the mechanical properties of Cu–Al alloys manufactured using the powder metallurgy technique to increase the rate of fabrication and characterization of new materials and provide physical insights into their properties.Six algorithms were used to construct the prediction models, with chemical composition and porosity of the compacts chosen as the descriptors.The results show that the sequential minimal optimization algorithm for support vector regression with a puk kernel(SMOreg/puk) model demonstrated the best prediction ability. Specifically, its predictions exhibited the highest correlation coefficient and lowest error among the predictions of the six models. The SMOreg/puk model was subsequently applied to predict the tensile strength and hardness of Cu–Al alloys and provide guidance for composition design to achieve the expected values. With the guidance of the SMOreg/puk model, Cu–12Al–6Ni alloy with a tensile strength(390 MPa) and hardness(HB 139) that reached the expected values was developed.

A novel approach to predict green density by high-velocity compaction based on the materials informatics method

High-velocity compaction is an advanced compaction technique to obtain high-density compacts at a compaction velocity of ≤10 m/s. It was applied to various metallic powders and was verified to achieve a density greater than 7.5 g/cm^3 for the Fe-based powders. The ability to rapidly and accurately predict the green density of compacts is important, especially as an alternative to costly and time-consuming materials design by trial and error. In this paper, we propose a machine-learning approach based on materials informatics to predict the green density of compacts using relevant material descriptors, including chemical composition, powder properties, and compaction energy. We investigated four models using an experimental dataset for appropriate model selection and found the multilayer perceptron model worked well, providing distinguished prediction performance, with a high correlation coefficient and low error values. Applying this model, we predicted the green density of nine materials on the basis of specific processing parameters. The predicted green density agreed very well with the experimental results for each material, with an inaccuracy less than 2%. The prediction accuracy of the developed method was thus confirmed by comparison with experimental results.

The materials data ecosystem： Materials data science and its role in data-driven materials discovery

Since its launch in 2011, the Materials Genome Initiative（MGI） has drawn the attention of researchers from academia,government, and industry worldwide. As one of the three tools of the MGI, the use of materials data, for the first time, has emerged as an extremely significant approach in materials discovery. Data science has been applied in different disciplines as an interdisciplinary field to extract knowledge from data. The concept of materials data science has been utilized to demonstrate its application in materials science. To explore its potential as an active research branch in the big data era, a three-tier system has been put forward to define the infrastructure for the classification, curation and knowledge extraction of materials data.

Microstructure and properties of nano-TiN modified Ti(C,N)-based cermets fabricated by powder injection molding and die pressing

Powder injection molding （PIM） and die pressing were employed to fabricate nano-TiN modified Ti（C,N）- based cermets. The shrinkage behavior, microstructure, porosity, and mechanical properties of the samples with and without nano-TiN addition fabricated by PIM and die pressing were analyzed. It is demonstrated that for either PIM or die pressing, the porosities are obviously reduced, the mechanical properties are significantly improved after adding nano-TiN, and the hard particles are refined; the rim phase thickness obviously becomes thinner, and the number of dimples in fracture also increases. Compared the samples fabricated by die pressing, it is difficult for PIM to obtain dense Ti（C,N）-based cermets. Due to the too much existence of pores and isolated carbon, the mechanical properties of the sintered samples by PIM are inferior to those of the sintered ones by die pressing.

Study on the impact force and green properties of high-velocity compacted aluminum alloy powder

High-velocity compaction （HVC） provides an effective means in the field of powder metallurgy （P/M） to reduce the porosity as well as to ameliorate the mechanical properties of products. In this study, the green density of an aluminum alloy is found to be 2.783 g cm 3. The ejection force for the aluminum alloy is in the range of 23 to 80 kN and the spring back is found to be less than 0.40%. The hardness of the green body is in the range of HRB 30 to 70. The bending strength of the green body is in the range of 6 to 26 MPa, which are higher than that of other aluminum alloys prepared by the traditional compaction method.

Injection molding and debinding of micro gears fabricated by micro powder injection molding

Micro powder injection molding （μPIM） was investigated for possible mass production of micro-components at rela- tively low cost. However, scaling down to such a level produces challenges in injection molding and debinding. Micro gears were fabricated by μPIM from in-house feedstock. The effect of injection speed and injection pressure on the replication of the micro gear cavity was investigated. Solvent debinding and thermal debinding processes were discussed. The results show that micro gears can be successfully fabricated under the injection pressure of 70 MPa and the 60% injection speed. Either too low or too high injection speed can cause incomplete filling of micro gears. The same is the case with too low injection pressure. Too high injection pressure can bring cracks. Solvent debinding of micro gears was performed in a mixture of petroleum ether and ethanol. Subsequently, micro gears were successfully debound by a multistep heating schedule.

Effect of Mn doping on mechanical properties and electronic structure of WCoB ternary boride by first-principles calculations

The first-principles calculations are performed to investigate the structural, mechanical property, hardness, and electronic structure of WCoB with 0, 8.33, 16.67, 25, and 33.33 at.% Mn doping content and W_2 CoB_2 with 0, 10, and 20 at.%Mn doping content. The cohesive energy and formation energy indicate that all the structures can retain good structural stability. According to the calculated elastic constants, Mn is responsible for the increase of ductility and Poisson＇s ratio and the decrease of Young＇s modulus, shear modulus, and bulk modulus. By using the population analysis and mechanical properties, the hardness is characterized through using the five hardness models and is found to decrease with the Mn doping content increasing. The calculated electronic structure indicates that the formation of a B–Mn covalent bond and a W–Mn metallic bond contribute to the decreasing of the mechanical properties.

Molecular identification and interaction assay of the gene(OsUbc13) encoding a ubiquitin-conjugating enzyme in rice

The ubiquitin （Ub）-conjugating enzyme, Ubc13, has been known to be involved in error-free DNA damage tolerance （or post-replication repair） via catalyzing Lys63-linked polyubiquitin chains formation together with a Ubc variant. However, its functions remain largely unknown in plant species, especial y in monocotyledons. In this study, we cloned a Ub-conjugating enzyme, OsUbc13, that shares the conserved domain of Ubc with AtUBC13B in Oryza sativa L., which encodes a protein of 153 amino acids; the deduced sequence shares high similarities with other homologs. Real-time quantitative polymerase chain reaction （PCR） indicated that OsUbc13 transcripts could be de-tected in al tissues examined, and the expression level was higher in palea, pistil, stamen, and leaf, and lower in root, stem, and lemma;the expression of OsUbc13 was induced by low temperature, methylmethane sulfate （MMS）, and H2O2, but repressed by mannitol, abscisic acid （ABA）, and NaCl. OsUbc13 was probably localized in the plasma and nuclear membranes. About 20 proteins, which are responsible for the positive yeast two-hybrid interaction of OsUbc13, were identified. These include the confirmed OsVDAC （correlated with apoptosis）, OsMADS1 （important for development of floral organs）, OsB22EL8 （related to reactive oxygen species （ROS） scavenging and DNA protection）, and OsCROC-1 （required for formation of Lys63 polyubiquitylation and error-free DNA damage tolerance）. The molecular characterization provides a foundation for the functional study of OsUbc13.