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. INDEX TERMS Artificial intelligence, big data, material selection, multilayer feedforward networks, neural network, property prediction, software-based web browser control. LIST OF SYMBOLS AND ABBREVIATIONS ADAM Adaptive Moment Estimation AI Artificial intelligence ANN Artificial Neural Networks β n ADAM algorithm parameter ADAM stability factor ε Prediction error of a neural network η ADAM step size f Error function g Gradient of the error function HTML HyperText Markup Language m ADAM first moment estimate v ADAM second moment estimate w Weights vector

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%.

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An educational software, called CalSev 1.0.2, has been developed for supporting the teaching in materials selection seminars. As a case study, a selection of materials for nuclear reactor pressure vessels has been chosen, because it is a representative example of the relationship between chemical composition, mechanical properties, and in-service behavior. This software has been developed to improve the students' understanding of the materials selection tasks, providing an interactive user-friendly platform that allows to modify-in real time-the content of chemical elements and mechanical requirements to obtain a materials performance evaluation. Due to its versatility, this informatics tool represents a great value for educational purposes, with special impact in blended and distance learning, by simplifying the arduous task of materials selection for a wide range of applications and promoting a collaborative working environment by easing the creation of small groups analyzing different study cases. Finally, to evaluate the functionality, interest, user-friendliness and the educational characteristics of CalSev 1.0.2, a survey among 36 undergraduate students has been carried out. K E Y W O R D Scomprehensive learning, computer-aided tool, educational support, materials selection, materialsEngineering is a practicing profession devoted to harnessing and modifying the three fundamental resources that humankind has available for the creation of all technology: energy, materials, and information [19]. In addition, nowadays, engineers need access to material attributes data and, since the quantity of data is large and the methods tedious to implement by hand, computer-based tools are necessary to enable the right selection of materials depending on the application [5].Selection of suitable materials for a given component is thus one of the hardest tasks to perform and hence, designers need to put their utmost effort in identifying the best materials with specific functionalities to fulfill the application requirements [39]. Design of components is associated with the Abbreviations: ASME B&PV, American society of mechanical engineers boiler and pressure vessels; JSME, Japanese society of mechanical engineers; KTA, Kerntechnischer Ausschuss (German nuclear commission); L e , experimental limit provided by key works; L s , standardized limit as described by the manufacturing codes; L s (Min) , minimum value of the standardized limits; L s (Max) , maximum value of the standardized limits; PWR, pressurized water reactors; RCC-MR, French code of nuclear design; RPV, reactor pressure vessels; SL/SLM, stringency level/stringency level methodology; SL (Max) , maximum stringency level; UTS, ultimate tensile strength (minimum required value); WORA, write once, run anywhere; XML, eXtensible markup language; Y p , yield point (minimum required value); σ max , maximum standard deviation.Comput Appl Eng Educ. 2018;26:125-140.wileyonlinelibrary.com/cae

The ability of a metal to be subjected to forming processes depends mainly on its plastic behavior and, thus, the mechanical properties belonging to this region of the stress–strain curve. Forming techniques are among the most widespread metalworking procedures in manufacturing, and aluminum alloys are of great interest in fields as diverse as the aerospace sector or the food industry. A precise characterization of the mechanical properties is crucial to estimate the forming capability of equipment, but also for a robust numerical modeling of metal forming processes. Characterizing a material is a very relevant task in which large amounts of resources are invested, and this paper studies how to optimize a multilayer neural network to be able to make, through machine learning, precise and accurate predictions about the mechanical properties of wrought aluminum alloys. This study focuses on the determination of the ultimate tensile strength, closely related to the strain hardening of a material; more precisely, a methodology is developed that, by randomly partitioning the input dataset, performs training and prediction cycles that allow estimating the average performance of each fully-connected topology. In this way, trends are found in the behavior of the networks, and it is established that, for networks with at least 150 perceptrons in their hidden layers, the average predictive error stabilizes below 4%. Beyond this point, no really significant improvements are found, although there is an increase in computational requirements.