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.

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.