Materials based on Ni-Co-Fe alloys, due to their excellent magnetic properties, attract great attention in nanotechnology, especially as candidates for high-density magnetic recording media and other applications from spintronic to consumer electronics. In this study, Ni-Co-Fe nanocrystalline coatings were electrodeposited from citrate-sulfate baths with the Ni²⁺:Co²⁺:Fe²⁺ ion concentration ratios equal to 15:1:1, 15:2:1, and 15:4:1. The effect of the composition of the bath on the morphology, microstructure, chemical composition, microhardness, and magnetic properties of the coatings was examined. Scanning (SEM) and transmission (TEM) electron microscopy, X-ray diffractometry (XRD), and energy dispersive X-ray spectroscopy (EDS) were used to study surface morphology, microstructure, chemical, and phase composition. Isothermal cross-sections of the Ni-Co-Fe ternary equilibrium system for the temperature of 50 °C and 600 °C were generated using the FactSage package. Magnetic properties were analyzed by a superconducting quantum interference device magnetometer (SQUID). All the coatings were composed of a single phase being face-centered cubic (fcc) solid solution. They were characterized by a smooth surface with globular morphology and a nanocrystalline structure of grain diameter below 30 nm. It was determined that Ni-Co-Fe coatings exhibit high hardness above 4.2 GPa. The measurements of hysteresis loops showed a significant value of magnetization saturation and small coercivity. The microstructure and properties of the obtained nanocrystalline coatings are interesting in terms of their future use in micromechanical devices (MEMS).

Experimental and modeling studies of the evolution of plate-like δ phase precipitates in Inconel 625 superalloy additively manufactured by the laser powder bed fusion process are performed. The maximum Feret diameter and the number of particles per unit area are used as parameters describing the size and distribution of the δ phase precipitates. On the basis of microstructural analysis and quantitative image analysis, the effect of time and temperature on the development of δ phase precipitates is determined. The distinct differences in the intensity of precipitation, growth, and coarsening of the δ phase precipitates during annealing at temperatures of 700 and 800 °C up to 2000 h are shown. The experimental results are compared with computational data obtained by thermodynamic modeling. Using the experimentally determined parameters of the δ phase precipitates in different variants of annealing, a fuzzy logic-based phase distribution model is designed. Since the quantity of available data was too small to train a model with the machine learning approach, expert knowledge is used to design the rules, while numerical data are used for its validation. Designed rules, as well as reasoning methodology are described. The proposed model is validated by comparing it with the experimental results. It can be used to predict the size and number density of the δ phase precipitates in the additively manufactured Inconel 625, subjected to long-term annealing at temperatures of 700–800 °C. Due to limited experimental data, the quality of assurance is not perfect, but warrants preliminary research. Graphical abstract

The process of continuous casting of steel is a complex technological task, including issues related to heat transfer, the steel solidification process, liquid metal flow and phase transitions in the solid state. This involves considerable difficulty in creating the optimal process control system, which would include the influence of all the physico-chemical phenomena which may occur. In parallel, there is an intensive development of new mathematical models and an increase in computer performance, therefore complex numerical simulations requiring substantial computing time can be conducted. This paper presents a review of currently applied numerical methods allowing the phenomena accompanying the process of continuous casting of steel to be accurately represented. Special attention was paid to the selection of appropriate methods to solve the technological problem selected. The possibilities of applying selected numerical models were analysed in order to modify and improve the existing process or to design a new one linked to the implementation of new steel grades in the current production. The description of the method of defining the boundary conditions, initial conditions and material parameters as vital components ensuring that numerical calculations based upon them in the finite element method, which is that most frequently applied, are correct is an important element of the paper. The possibility of reliably defining the values of boundary parameters on the basis of information on the intensity of cooling in individual zones of the continuous casting machine was analysed.

In our study, the comparison of the automatically detected precipitates in L-PBF Inconel 625, with experimentally detected phases and with the results of the thermodynamic modeling was used to test their compliance. The combination of the complementary electron microscopy techniques with the microanalysis of chemical composition allowed us to examine the structure and chemical composition of related features. The possibility of automatic detection and identification of precipitated phases based on the STEM-EDS data was presented and discussed. The automatic segmentation of images and identifying of distinguishing regions are based on the processing of STEM-EDS data as multispectral images. Image processing methods and statistical tools are applied to maximize an information gain from data with low signal-to-noise ratio, keeping human interactions on a minimal level. The proposed algorithm allowed for automatic detection of precipitates and identification of interesting regions in the Inconel 625, while significantly reducing the processing time with acceptable quality of results.