Numerical simulation of metal forming processes is finding increasingly wide applications in advanced industry for the optimization of material processing conditions and prediction of process parameters, finally delivering a reduction of production costs. This work presents a comparison between simulation results of radial shear rolling (RSR) of VT3-1 titanium alloy (Ti-Al-Mo-Cr-Fe-Si) and results of experimental RSR at 1060 °C, 980 °C, and 900 °C in one, three, and five passes, respectively. The digital model (DM) demonstrates a high convergence of the calculation results (calculation error of less than 5%) with the actual geometric parameters of the experimental bars, their surface temperature, and rolling time during the experiment, which indicates a good potential for its application in the selection of deformation modes. Based on the simulation and experimental data, the conditions providing for the formation of differently sized grains in the bar cross-section have been identified. All of the as-rolled bars exhibit a gradient distribution of macrostructure grain size number (GSN), from the smallest one at the bar surface (2–4) to the greatest one in the center (4–6). The macrostructure GSN correlates with the workpiece temperature, which is the highest in the axial zone of the bars, and with the experimentally observed high plastic strain figures in the surface layers. It was found that, depending on the temperature conditions and reduction ratio per pass, any minor change in the values of process parameters can lead to the formation of macrostructures with different grain size numbers.

A system of slow convective thermals is considered in a vicinity of convection level. It is shown that a system of spherical convective thermals of fixed radius can be described by a stochastic equation, which is practically identical to Langevin equation for an ensemble of classical Brownian particles. Under the proposed approach, it was shown that the mean vertical velocity of a system of thermals at the convection level is zero. A kinetic analogue of thermodynamic temperature, corresponding to the second moment of the vertical velocity, was shown to exist for a system of thermals. Moreover, the associated Fokker–Planck equation for the system of slow convective thermals has a stationary solution corresponding to generalized Maxwell distribution over vertical velocities. Numerical and field experiments convincingly show that the generalized Maxwell equations is in agreement with the results of calculations and atmospheric measurements.

In this paper, a computer simulation of round wire drawing processes with different equations of state for steel A12 has been carried out. In addition, the methods of improving the configuration of neural networks based on multilayer perceptron (MLP) for estimating the distributions of residual stress tensor components have been investigated. The study demonstrated that the strain rate exerted a significant influence on the character of the processes, particularly within the central region (0r–0.4r) of the investigated specimens. In addition, the employment of software tools for the purpose of tuning the hyperparameters of trained machine learning models, including Optuna, BayesianOpt, and Skopt, has been demonstrated to enhance the predictive capability of the models. Consequently, this results in an improvement in the accuracy of the obtained distributions of the required characteristics.

Introduction. The quality of rolled reinforcement is a key factor in the reliability of reinforced concrete structures in conditions of increasing number of floors and complexity of construction projects. Modern Russian standards (State Standard 34028-2016, State Standard R 52544-2006) require strict statistical control of the mechanical properties of rolled products, including temporary resistance, yield strength and relative elongation. The relevance of the study is due to the need to improve the accuracy of sampling methods in order to minimize the risks of an unreliable quality assessment. Aim. Development of a methodology for selective quality control of rebar products based on statistical analysis, including verification of the normality of data distribution, elimination of outliers and assessment of compliance with the requirements of standards. Materials and methods. The study is based on the analysis of test data for rebar rolled products of class A600 (State Standard 34028-2016). The Shapiro–Wilk, Pearson (χ2), and Kolmogorov–Smirnov criteria were applied to verify the normality of the distribution. Outliers were identified by the Charlier method (α = 0.05). Software tools were used to calculate statistical indicators (mean, RMSD, skewness, excess) and visualization (histograms, Q–Q graphs). Results. It was found that the presence of outliers distorts the distribution of key parameters. For the ratio of temporary resistance to yield strength, the exclusion of emissions allowed the normality of the distribution to be restored (p > 0.05 according to two criteria), whereas for the relative elongation δmax, their effect turned out to be insignificant. Automated analysis has confirmed that the use of confidence intervals (95 %) and State Standard 34028-2016 methods increases the reliability of quality assessment. Conclusions. Eliminating of outliers is a critical step to ensure the normality of data distribution. The combined use of agreement criteria increases the reliability of statistical conclusions. Automation of the methodology based on specialized software reduces the monitoring time and minimizes the human factor. Regular sampling is necessary to ensure that products meet the requirements of the standards and reduce risks in construction.