In metal forming, the plastic behavior of metallic alloys is directly related to their formability, and it has been traditionally characterized by simplified models of the flow curves, especially in the analysis by finite element simulation and analytical methods. Tools based on artificial neural networks have shown high potential for predicting the behavior and properties of industrial components. Aluminum alloys are among the most broadly used materials in challenging industries such as aerospace, automotive, or food packaging. In this study, a computer-aided tool is developed to predict two of the most useful mechanical properties of metallic materials to characterize the plastic behavior, yield strength and ultimate tensile strength. These prognostics are based on the alloy chemical composition, tempers, and Brinell hardness. In this study, a material database is employed to train an artificial neural network that is able to make predictions with a confidence greater than 95%. It is also shown that this methodology achieves a performance similar to that of empirical equations developed expressly for a specific material, but it provides greater generality since it can approximate the properties of any aluminum alloy. The methodology is based on the usage of artificial neural networks supported by a big data collection about the properties of thousands of commercial materials. Thus, the input data go above 2000 entries. When the relevant information has been collected and organized, an artificial neural network is defined, and after the training, the artificial intelligence is able to make predictions about the material properties with an average confidence greater than 95%.

Aluminum alloys are among the most widely used materials in demanding industries such as aerospace, automotive or food packaging and, therefore, it is essential to predict the behavior and properties of each component. Tools based on artificial intelligence can be used to face this complex problem. In this work, a computer-aided tool is developed to predict relevant mechanical properties of aluminum alloys—Young’s modulus, yield stress, ultimate tensile strength and elongation at break. These predictions are based on the alloy chemical composition and tempers, and are employed to estimate the bilinear approximation of the stress-strain curve, very useful as a decision tool that helps in the selection of materials. The system is based on the use of artificial neural networks supported by a big data collection about technological characteristics of thousands of commercial materials. Thus, the volume of data exceeds 5 k entries. Once the relevant data have been retrieved, filtered and organized, an artificial neural network is defined and, after the training, the system is able to make predictions about the material properties with an average confidence greater than 95 % . Finally, the trained network is employed to show how it can be used to support decisions about engineering applications.

During their useful life, polymers are subject to degradation processes due to exposure to specific environmental conditions over long times. These processes generally lead to changes, almost always irreversible, of properties and performances of polymers, changes which would be useful to be able to predict in advance. To meet this need, numerous investigations have been focused on the possibility to predict the long-term performance of polymers, if exposed to specific environments, by the so called “accelerated aging” tests. In such procedures, the long-term behavior of polymeric materials is typically predicted by subjecting them to cycles of radiations, temperatures, vapor condensation, and other external agents, at levels well above those found in true conditions in order to accelerate the degradation of polymers: this can produce effects that substantially deviate from those observable under natural exposure. Even following the standard codes, different environmental parameters are often used in the diverse studies, making it difficult to compare different investigations. The correlation of results from accelerated procedures with data collected after natural exposure is still a debated matter. Furthermore, since the environmental conditions are a function of the season and the geographical position, and are also characteristic of the type of exposure area, the environmental parameters to be used in accelerated aging tests should also consider these variables. These and other issues concerning accelerated aging tests applied to polymers are analyzed in the present work. However, bearing in mind the limitations of these practices, they can find useful applications for rating the durability of polymeric materials.

This paper investigates the extrusion process to manufacture bimetallic cylinders combining a magnesium alloy core (AZ31B) and a titanium alloy sleeve (Ti6Al4V) of interest in aeronautical applications. A robust finite element model has been developed to determine the most influential parameters and to study the effect of them on the extrusion force and damage induced by means of Design of Experiments (DOE) and Taguchi method. The results show that the most influential parameters in the extrusion forces are the friction between sleeve and container/die and the height of the cylinder; and the less influential ones are the process temperature and ram speed. Moreover, minimum values of forces along with low damage can be reached by favorable interface contact conditions, minimizing the friction at the core-container/die interface, as the main influencing factor; followed by the geometrical dimensions of the billet, being the billet height more important when paying attention to the minimum forces, and being the core diameter when considering the minimum damage as the most important criterion. The results can potentially be used to improve the efficiency of this kind of extrusion process and the quality of the extruded part that, along with the use of lightweight materials, can contribute to sustainable production approaches.

The growth of green energy technologies within the frame of the 7th Sustainable Development Goal (SDG) along with the concern about climatic changes make nuclear energy an attractive choice for many countries to ensure energy security and sustainable development as well as to actively address environmental issues. Unlike nuclear equipment (immovable goods), which are often well-catalogued and analyzed, the design and manufacturing codes and their standardized materials specifications can be considered movable and intangible goods that have not been thoroughly studied based on a detailed evaluation of the scientific and technical literature on the reactor pressure vessel (RPV) materials behavior. The aim of this work is the analysis of historical advances in materials properties research and associated standardized design codes requirements. The analysis, based on the consolidated U.S. Nuclear Regulatory Commission (NRC) Regulatory Guide (RG) 1.99 Rev.2 model, enables determination of the best materials options, corresponding to some of the most widely used material specifications such as WWER 15Kh2MFAA (used from the 1970s and 1980s; already in operation), ASME SA-533 Grade B Cl.1 (used in pressurized water reactor-PWR 2nd–4th; already in operation), DIN 20MnMoNi55 and DIN 22NiMoCr37 (used in PWR 2nd–4th) as well as ASTM A-336 Grade F22V (current designs). Consequently, in view of the results obtained, it can be concluded that the best options correspond to recently developed or well-established specifications used in the design of pressurized water reactors. These assessments endorse the fact that nuclear technology is continually improving, with safety being its fundamental pillar. In the future, further research related to the technical heritage from the evolution of materials requirements for other clean and sustainable power generation technologies will be performed.