One of the main stages in the pipes production from low-carbon steel grades is hardening. In the course of quenching with standard modes, the metal structure changes and, as a consequence, the mechanical properties change. Comparing the indicators values of these properties, such as hardness, strength, ductility, etc., makes it possible to judge the choice correctness of hardening modes. Therefore, it is important to select in advance the optimal heat treatment modes in order to obtain metal with desired mechanical properties. There are many theories and approximations that allow predicting the values of mechanical properties under given initial conditions, such as chemical composition, grain size, heating temperature, cooling rate, etc., however, in most cases they are either not accurate or tied to a specific production unit and, as a result, are not suitable for use in other (different) conditions. The purpose of this work is to build a model for predicting the hardness of pipe steels after quenching using modern machine learning methods. As an initial sample, an aggregated array of experimental data is used, collected mainly from supercooled austenite decomposition diagrams, as well as tables and other data obtained from various sources. This paper describes in detail the stage of data preprocessing, model construction and validation, as well as the comparison of the simulation result with the results of similar work.

This study investigates the effect of complex nanomodification combined with the simultaneous application of magnetic fields and mechanical vibration on the structure formation and performance properties of medium-alloy steel 40CrNi3MoV. Improving the structural homogeneity and operational characteristics of such steels remains an important task due to their widespread use in components operating under severe loading and wear conditions. The introduction of the nanostructured modifier InSteel-7 at a concentration of 0.03%, together with simultaneous magnetic and vibrational treatment of the melt, resulted in pronounced structural homogenization and grain refinement. Quantitative metallographic analysis using Thixomet Pro image analyzer revealed a significant refinement of the dendritic structure, with the secondary dendrite arm spacing decreasing from 73.9 μm to 27.9 μm. X-ray phase analysis confirmed the preservation of phase composition while indicating increased structural uniformity of the BCC matrix. Energy-dispersive spectroscopy and elemental micro-mapping demonstrated high chemical purity of the alloy and a uniform distribution of the modifier components. The combined treatment significantly improved the mechanical and tribological characteristics of the material. The average hardness increased from 390 HV to 510 HV, while tribological tests showed a reduction in wear track depth from 5.16 μm to 0.87 μm and a decrease in surface roughness from Ra 2.13 μm to 0.20 μm, indicating enhanced wear resistance.