Stress-strain curves of the EN AW 6082 aluminium alloy with 1.2 Si-0.51 Mg-0.75 Mn (wt.%) were determined by the uniaxial compression tests at temperatures of 450–550 °C with a strain rate of 0.5–10 s⁻¹. The initial structure state corresponded to three processing types: as-cast structure non-homogenized or homogenized at 500 °C, and the structure after homogenization and hot extrusion. Significantly higher flow stress appeared as a result of low temperature forming of the non-homogenized material. Hot deformation activation energy Q-values varied between 99 and 122 kJ·mol⁻¹ for both homogenized materials and from 200 to 216 kJ·mol⁻¹ for the as-cast state, while the Q-values calculated from the measured steady-state stress were always higher than those calculated from the peak stress values. For the extruded state of the 6082 alloy, the physically-based model was developed to reliably predict the flow stress influenced by dynamic softening, temperature, strain rate, and true strain up to 0.6.

Based on dilatometric tests, the effect of various values of previous deformation on the kinetics of austenite transformations during the cooling of 100Cr6 steel has been studied. Dilatometric tests have been performed with the use of the optical dilatometric module of the plastometer Gleeble 3800. The obtained results were compared to metallographic analyses and hardness measurements HV30. Uniaxial compression deformations were chosen as follows: 0, 0.35, and 1; note that these are true (logarithmic) deformations. The highly important finding was the absence of bainite. In addition, it has been verified that with the increasing amount of deformation, there is a further shift in the pearlitic region to higher cooling rates. The previous deformation also affected the temperature martensite start, which decreased due to deformation. The deformation value of 1 also shifted the critical cooling rate required for martensite formation from the 12 °C/s to 25 °C/s.

In the presented research, conventional hot processing maps superimposed over the flow stress maps or activation energy maps are utilized to study a correlation among the efficiency of power dissipation, flow stress, and activation energy evolution in the case of Cr-Mo low-alloyed steel. All maps have been assembled on the basis of two flow curve datasets. The experimental one is the result of series of uniaxial hot compression tests. The predicted one has been calculated on the basis of the subsequent approximation procedure via a well-adapted artificial neural network. It was found that both flow stress and activation energy evolution are capable of expressing changes in the studied steel caused by the hot compression deformation. A direct association with the course of power dissipation efficiency is then evident in the case of both. The connection of the presence of instability districts to the activation energy evolution, flow stress course, and power dissipation efficiency was discussed further. Based on the obtained findings it can be stated that the activation energy processing maps represent another tool for the finding of appropriate forming conditions and can be utilized as a support feature for the conventionally-used processing maps to extend their informative ability.

This paper deals with a study of additively manufactured (by the Selective Laser Melting, SLM, method) and conventionally produced AISI 316L stainless steel and their comparison. With the intention to enhance the performance of the workpieces, each material was post-processed via hot rotary swaging under a temperature of 900 °C. The samples of each particular material were analysed regarding porosity, microhardness, high cycle fatigue, and microstructure. The obtained data has shown a significant reduction in the residual porosity and the microhardness increase to 310 HV in the sample after the hot rotary swaging. Based on the acquired data, the sample produced via SLM and post-processed by hot rotary swaging featured higher fatigue resistance compared to conventionally produced samples where the stress was set to 540 MPa. The structure of the printed samples changed from the characteristic melting pools to a structure with a lower average grain size accompanied by a decrease of a high fraction of high-angle grain boundaries and higher geometrically necessary dislocation density. Specifically, the grain size decreased from the average diameters of more than 20 µm to 3.9 µm and 4.1 µm for the SLM and conventionally prepared samples, respectively. In addition, the presented research has brought in the material constants of the Hensel-Spittel formula adapted to predict the hot flow stress evolution of the studied steel with respect to its 3D printed state.

In engineering practice, it is often necessary to determine functional relationships between dependent and independent variables. These relationships can be highly nonlinear, and classical regression approaches cannot always provide sufficiently reliable solutions. Nevertheless, Machine Learning (ML) techniques, which offer advanced regression tools to address complicated engineering issues, have been developed and widely explored. This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials. The ML-based regression methods of Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Decision Tree Regression (DTR), and Gaussian Process Regression (GPR) are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel. Although the GPR method has not been used for such a regression task before, the results showed that its performance is the most favorable and practically unrivaled; neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.

Designing a composite, possibly strengthened by a dispersion of (fine) oxides, is a favorable way to improve the mechanical characteristics of Cu while maintaining its advantageous electric conductivity. The aim of this study was to perform mechanical alloying of a Cu powder with a powder of Al₂O₃ oxide, seal the powder mixture into evacuated Cu tubular containers, i.e., cans, and apply gradual direct consolidation via rotary swaging at elevated temperatures, as well as at room temperature (final passes) to find the most convenient way to produce the designed Al₂O₃ particle-strengthened Cu composite. The composites swaged with the total swaging degree of 1.83 to consolidated rods with a diameter of 10 mm were subjected to measurements of electroconductivity, investigations of mechanical behavior via compression testing, and detailed microstructure observations. The results revealed that the applied swaging degree was sufficient to fully consolidate the canned powders, even at moderate and ambient temperatures. In other words, the final structures, featuring ultra-fine grains, did not exhibit voids or remnants of unconsolidated powder particles. The swaged composites featured favorable plasticity regardless of the selected processing route. The flow stress curves exhibited the establishment of steady states with increasing strain, regardless of the applied strain rate. The electroconductivity of the composite swaged at elevated temperatures, featuring homogeneous distribution of strengthening oxide particles and the average grain size of 1.8 µm², reaching 80% IACS (International Annealed Copper Standard).