It is known that the yttria-stabilized zirconia (YSZ) material has superior thermal, mechanical, and electrical properties. This material is used for manufacturing products and components of air heaters, hydrogen reformers, cracking furnaces, fired heaters, etc. This work is aimed at searching for the optimal sintering mode of YSZ ceramics that provides a high crack growth resistance. Beam specimens of ZrO₂ ceramics doped with 6, 7, and 8 mol% Y₂O₃ (hereinafter: 6YSZ, 7YSZ, and 8YSZ) were prepared using a conventional sintering technique. Four sintering temperatures (1450 °C, 1500 °C, 1550 °C, and 1600 °C) were used for the 6YSZ series and two sintering temperatures (1550 °C and 1600 °C) were used for the 7YSZ and 8YSZ series. The series of sintered specimens were ground and polished to reach a good surface quality. Several mechanical tests of the materials were performed, namely, the microhardness test, fracture toughness test by the indentation method, and single-edge notch beam (SENB) test under three-point bending. Based on XRD analysis, the phase balance (percentages of tetragonal, cubic, and monoclinic ZrO₂ phases) of each composition was substantiated. The morphology of the fracture surfaces of specimens after both the fracture toughness tests was studied in relation to the mechanical behavior of the specimens and the microstructure of corresponding materials. SEM-EDX analysis was used for microstructural characterization. It was found that both the yttria percentage and sintering temperature affect the mechanical behavior of the ceramics. Optimal chemical composition and sintering temperature were determined for the studied series of ceramics. The maximum transformation toughening effect was revealed for ZrO₂-6 mol% Y₂O₃ ceramics during indentation. However, in the case of a SENB test, the maximum transformation toughening effect in the crack tip vicinity was found in ZrO₂-7 mol% Y₂O₃ ceramics. The conditions for obtaining YSZ ceramics with high fracture toughness are discussed.

Laser alloying is an effective method to form functional surface layers (coatings) on metallic materials, particularly on stainless steels. Unique phase balance, dislocation substructure, and a possibility to obtain gradient microstructures after laser alloying slow down the surface degradation and increase the wear resistance. In this work, the optimal parameters of laser alloying and their effects on microstructure and properties were investigated for two stainless steels: ferritic AISI 420 and austenitic AISI 304. Three types of alloying plasters were used: 85Nb + 15 graphite, 85Nb + 15 liquid glass, and 15Fe + 30Ni + 20B + 10Si + 25 liquid glass (wt.%). The laser power density of 0.3 × 10⁵ W/cm² and beam scanning speed of 1990 mm/min were found to generate 220–320 μm thick coatings with complex microstructures. Phase balance in the coatings was studied with X-ray diffraction and magnetometric phase analyses. High microhardness (up to 16 GPa) and wear resistance were associated with the formation of martensite with some retained austenite and Nb-, Cr-, Si-, and B-rich particles in the surface layer of AISI 420 steel, and high dislocation density austenite strengthened with Ti-, Nb-, Cr-, and Si-rich particles in AISI 304 steel.

Purpose: The main aim of this paper is development, software implementation and use of the alloys selection method for the design of biocompatible materials in medical production. It is based on the use of Ito decomposition and Logistic Regression. Design/methodology/approach: The technology of machine learning is used to solve the task. The developed classification method is based on the application of multiclass Logistic Regression. In order to reduce the probability of incorrect alloy identification, expansion of the input characteristics based on the Ito decomposition of the second order has been made. On the one hand, it increased the dimension of the input features space, and as a result, it increased the time for training procedure, but on the other, it increased the solution accuracy of the alloys selection task. The accuracy evaluation of the method was carried out using different criteria. In particular, the method accuracy was estimated based on the ratio of correctly classified titanium alloys samples to the test sample dimension. This measure was used to assess the classification accuracy in the training and test modes. For a more detailed analysis of the classification method results, two additional measures were further used: Precision and Recall. Their calculation was based on the constructed confusion matrix. This made it possible to assess the ability of the developed method to find the instances of each individual alloy as a whole, as well as the ability to distinguish instances of one class from representatives on the other. The combination of these indicators allowed to evaluate the classification task accuracy in the conditions of the imbalance dataset for each class of the investigated material separately. Findings: The simulation results confirmed the effectiveness of the use of machine learning tools to solve this task. High indicators of the method’s accuracy based on the experimental results were established. In particular, the overall accuracy of the method is 96.875%, and the average values of Precision and Recall for all four classes are 94% and 98% respectively. Expansion of each vector's features from the training dataset by using Ito decomposition increased the method accuracy by more than 33% compared to the basic Logistic Regression. Research limitations/implications: The Logistic Regression's training procedure, as well as the increase of the space size of the investigated alloy's input features by using Ito decomposition, imposes a number of limitations on the application of the method in tasks that depend on the duration of the work. Practical implications: The proposed machine learning approach foralloys selection allows reducing the time, material and human resources needed to investigate the titanium alloys properties. The developed method increases the accuracy of the selection alloys task compared to the four known methods, an average of 14.5%. It can be used to select materials with appropriate properties for the design of biocompatible medical products. Originality/value: A method and software product for the titanium alloys classification task using a supervised learning technique has been developed. For this aim, the method of Logistic Regression with expanding inputs based on the second-order Ito decomposition is used.