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.

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 $95\%$.

One of the challenges in additive manufacturing (AM) of metallic materials is to obtain workpieces free of defects with excellent physical, mechanical, and metallurgical properties. In wire and arc additive manufacturing (WAAM) the influences of process conditions on thermal history, microstructure and resultant mechanical and surface properties of parts must be analyzed. In this work, 3D metallic parts of mild steel wire (American Welding Society-AWS ER70S-6) are built with a WAAM process by depositing layers of material on a substrate of a S235 JR steel sheet of 3 mm thickness under different process conditions, using as welding process the gas metal arc welding (GMAW) with cold metal transfer (CMT) technology, combined with a positioning system such as a computer numerical controlled (CNC) milling machine. Considering the hardness profiles, the estimated ultimate tensile strengths (UTS) derived from the hardness measurements and the microstructure findings, it can be concluded that the most favorable process conditions are the ones provided by CMT, with homogeneous hardness profiles, good mechanical strengths in accordance to conditions defined by standard, and without formation of a decohesionated external layer; CMT Continuous is the optimal option as the mechanical properties are better than single CMT.

In this work, a computer-aided tool is developed to predict relevant physical and mechanical properties that are involved in the selection tasks of metallic materials. The system is based on the use of artificial neural networks supported by big data collection of information about the technological characteristics of thousands of materials. Thus, the volume of data exceeds 43k. The system can access an open online material library (a website where material data are recorded), download the required information, read it, filter it, organise it and move on to the step based on artificial intelligence. An artificial neural network (ANN) is built with thousands of perceptrons, whose topology and connections have been optimised to accelerate the training and predictive capacity of the ANN. After the corresponding training, the system is able to make predictions about the material density and Young’s modulus with average confidences greater than 99% and 98%, respectively.

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 $5k$ 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.