First-Principles Calculations of Y-Si-O Nanoclusters and Effect of Si on Microstructure and Mechanical Properties of 12Cr ODS Steel in Vacuum Sintering System

High density of thermally stable Y-Si-O nanoparticles dispersed in the Fe matrix play a primary role in oxide dispersion strengthened (ODS) steel. In this study, the binding energies of solutes Y, O and Si with vacancies have been calculated in the framework of first-principles density functional theory. According to the calculations, any two solutes of Y, O and Si bound with each other strongly in the second nearest neighboring (NN) sites while not in 1NN. A vacancy (v) bounds strongly with Y and O in 1NN site. The binding sequence of solutes with v followed O-v → Y-v → Si-v, and the affinity of Y, Si and v with O followed O-Y → O-v → O-Si. The nucleation mechanism of Y-O-Si nanoclusters was determined, which gave the feasibility of adding Si to ODS steels. The core (consisting of Si and O)-shell (enriched Fe and Cr) structure of the microparticles was found in ODS steels containing Si, fabricated by mechanical alloying (MA) and vacuum sintering. Moreover the nanoparticles of monoclinic cubic Y2O3, Y2SiO5 and Y2Si2O7 with sizes of 5 ~ 12 nm were observed in ODS steel. Si reduced the sintering temperature by maximizing densities and mechanical properties at a lower sintering temperature. The steel with 3 wt% Si was sintered at 1280 °C, exhibiting the best comprehensive mechanical properties. The tensile strength, hardness and relative density were 1025 MPa, 442.44 HV and 95.3%, respectively.

Substantial Improvement of High Temperature Strength of New-Generation Nano-Oxide-Strengthened Alloys by Addition of Metallic Yttrium

Oxide-dispersion-strengthened (ODS) Fe-Al-Y2O3-based alloys (denoted as FeAlOY) containing 5 vol. % of nano-oxides have a potential to become top oxidation and creep-resistant alloys for applications at temperatures of 1100–1300 °C. Oxide dispersoids cause nearly perfect strengthening of grains; thus, grain boundaries with limited cohesive strength become the weak link in FeAlOY in this temperature range. One of the possibilities for significantly improving the strength of FeAlOY is alloying with appropriate elements and increasing the cohesive strength of grain boundaries. Nearly 20 metallic elements have been tested with the aim to increase cohesive strength in the frame of preliminary tests. A positive influence is revealed for Al, Cr, and Y, whereby the influence of Y is enormous (addition of 1% of metallic Y increases strength by a factor of 2), as it is presented in this paper.

SANS study on the stability of oxide nanoparticles in long-term thermally aged 9Cr-ODS steel

A 9Cr-oxide dispersion strengthened (ODS) steel was thermally aged at 873 K for up to 5000 hours. The size distribution and chemical composition of the dispersed oxide nanoparticles were analyzed by small angle neutron scattering (SANS) method under magnetic field. Combined with transmission electron microscopy (TEM), Vickers micro-hardness tests and electron backscattered diffraction (EBSD) measurements, all the results showed that the thermal treatment had little or no effect on the size distributions and volume fractions of the oxide nanoparticles in the ferromagnetic matrix, which suggested excellent thermal stability of the 9Cr-ODS steel.

The Precipitated Particle Refinement in High-Cr ODS Steels by Microalloying Element Addition

Two oxide-dispersion-strengthened (ODS) steels with different compositions (14Cr-ODS and 14Cr-Zr-ODS) were investigated to reveal the influences of microalloying element addition on the microstructure and to clarify the refining mechanism of precipitated particles. TEM and HRTEM results indicated that precipitated particles in the Zr-containing ODS steel had finer sizes and dispersed more homogeneously within the grains. It was found that rhombohedral Y4Zr3O12 particles with complex lattice structures were formed and could pin the migration of the grain boundaries during heat treatment due to their high thermal stability. In addition, the Zr-containing ODS steel exhibited a finer and more uniform grain morphology. Tensile tests showed that microalloying element addition could significantly improve the comprehensive mechanical properties of 14Cr ODS steels at room temperature.

Experimental Investigation of EU-DEMO Breeding Blanket First Wall Mock-Ups in Support of the Manufacturing and Material Development Programmes

This paper presents the testing campaign of the two First Wall mock-ups in the HELOKA facility, one mock-up having a 3 mm thick Oxide Dispersion Strengthened (ODS) steel layer on its surface and the other featuring a tungsten functionally graded cover. Special consideration is given to the diagnostics used for these tests, in particular, the measurement of the surface temperature of the tungsten functionally graded layer with an infrared camera. Additionally, the paper looks into the uncertainty associated with the calorimetric evaluation of the applied heating power for these experiments.

Development of Machine Learning Models to Evaluate the Toughness of OPH Alloys

Oxide Precipitation-Hardened (OPH) alloys are a new generation of Oxide Dispersion-Strengthened (ODS) alloys recently developed by the authors. The mechanical properties of this group of alloys are significantly influenced by the chemical composition and appropriate heat treatment (HT). The main steps in producing OPH alloys consist of mechanical alloying (MA) and consolidation, followed by hot rolling. Toughness was obtained from standard tensile test results for different variants of OPH alloy to understand their mechanical properties. Three machine learning techniques were developed using experimental data to simulate different outcomes. The effectivity of the impact of each parameter on the toughness of OPH alloys is discussed. By using the experimental results performed by the authors, the composition of OPH alloys (Al, Mo, Fe, Cr, Ta, Y, and O), HT conditions, and mechanical alloying (MA) were used to train the models as inputs and toughness was set as the output. The results demonstrated that all three models are suitable for predicting the toughness of OPH alloys, and the models fulfilled all the desired requirements. However, several criteria validated the fact that the adaptive neuro-fuzzy inference systems (ANFIS) model results in better conditions and has a better ability to simulate. The mean square error (MSE) for artificial neural networks (ANN), ANFIS, and support vector regression (SVR) models was 459.22, 0.0418, and 651.68 respectively. After performing the sensitivity analysis (SA) an optimized ANFIS model was achieved with a MSE value of 0.003 and demonstrated that HT temperature is the most significant of these parameters, and this acts as a critical rule in training the data sets.