Merayo Fernández, DavidRodríguez Prieto, ÁlvaroCamacho López, Ana María2024-05-202024-05-202020-07-062075-4701http://doi.org/10.3390/met10070904https://hdl.handle.net/20.500.14468/12369Aluminum 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.eninfo:eu-repo/semantics/openAccessjournal articlealuminum alloybig dataartificial intelligencemulti-layer artificial neural networkpythonstress-strain curvematerial selectiondecision support systemmaterial characterization