For Inconel 625, where the γ” and δ phases precipitate, the influence of prior hot rolling on the process is not well covered. The influence of segregation bands and prior hot rolling on the precipitation of secondary phases during aging at 750 °C for different times was investigated. Prior hot-rolling was conducted on a hot rolling mill at 1050 °C and 1150 °C with three different deformation levels. The hot rolled samples were aged at 750 °C for 1, 5, 25 and 125 h. The γ″ precipitated in both the deformed and recrystallized grains in the segregation bands containing a high concentration of niobium and molybdenum and a lower concentration of nickel, chromium and iron. The opposite was observed between the segregation bands where no γ″ precipitate was found. There was a smooth transition in the density and the size of the γ″ particles in the deformed grains at the border of the segregation bands, while a more complex transition occurred in the recrystallized grains. This occurred in the area where the average niobium concentration decreased from 4.5 to 2.7 wt. %, which influenced the mechanical properties.

In this paper, the Conditional Average Estimator artificial neural network (CAE ANN) was used to analyze the influence of chemical composition in conjunction with selected process parameters on the yield strength and elongation of an extruded 6082 aluminum alloy (AA6082) profile. Analysis focused on the optimization of mechanical properties as a function of casting temperature, casting speed, addition rate of alloy wire, ram speed, extrusion ratio, and number of extrusion strands on one side, and different contents of chemical elements, i.e., Si, Mn, Mg, and Fe, on the other side. The obtained results revealed very complex non-linear relationships between all of these parameters. Using the proposed approach, it was possible to identify the combinations of chemical composition and process parameters as well as their values for a simultaneous increase of yield strength and elongation of extruded profiles. These results are a contribution of the presented study in comparison with published research results of similar studies in this field. Application of the proposed approach, either in the research and/or in industrial aluminum production, suggests a further increase in the relevant mechanical properties.

Mathematical descriptions of true stress/true strain curves, experimentally obtained on cylindrical specimens under hot compressive conditions, are of great importance and are widely investigated. An additional black-box modelling approach using transfer functions (TF) is tested. For tested 51CrV4 steel, a TF of third order is employed for description of true stress (output) depending on the strain rate (input). Sets of TF coefficients are determined using numerical optimization techniques for each testing temperature and strain rate. To avoid scattering of TF parameters, time in Laplacian transformation is replaced with strain, while TF input is the strain rate. Obtained models cover deformations starting practically from zero to 0.7. Average absolute relative error for models based on TF of the third order and of the second order are 0.93% and 3.64%.

In this study, conditional average estimator neural networks (CAE NNs) were used for an analysis of the common influences of the cooling mode in relation to the ram speed, extrusion ratio, casting speed and casting temperature on the yield strength and the elongation of an extruded profile made from aluminium alloy (AA)6082. The obtained results from the analysis revealed very complex relationships between these parameters. In order to maximise the values for the yield strength and the elongation, the values for the ram speed, extrusion ratio, casting speed and casting temperature should be optimised in relation to the mode of cooling.

Additive manufacturing is a complicated process, including powder preparation and post-processing. The atmosphere during the processing of sensitive alloys like Ti6Al4V is among the most important factors. The N content influences the mechanical properties in a significant way. Therefore, it is essential to understand, recognise, and identify the problems in case of machine malfunction or human error. The influence of a low-quality atmosphere on the microstructure evolution and mechanical properties was investigated during the direct energy deposition of hybrid additive-manufactured Ti6Al4V. The properties of the built parts were characterised by chemical analyses, light and electron microscopy, and mechanical testing. High concentrations of N, which absorbed and dissolved with every deposition layer of the direct-energy-deposition process, promoted the formation of a layer of α-scale on the part’s surface. The presence of α-scale was confirmed by observing the sample surfaces and the lack of fusion pores where a layer of α-scale remained. The α-scale partially or entirely dissolved during the next deposition layer. The morphology of the partially melted, α-scale depended on the size of the deposition and, thus, on the temperature evolution during the direct energy deposition.

The hot deformation of metal as a nonlinear system is mathematically described by a local linear model associated with the working conditions using a transfer function (TF) in the Laplace domain. Experimental data (true stress vs. true strain curves) are obtained using the established compressive uniaxial deformation test, where experimental conditions (strain rate and temperature) define the working conditions of the local linear TF model, which is intrinsically a function of strain. Based on the TF model, three important physical quantities of the tested metal are determined exactly: the work done per unit deformation, the average flow stress, and the flow-stress derivative with respect to the strain based on a particular TF. The exactly determined quantities, determined as a function of strain, can replace the previously used approximations in some rolling force and torque calculations.