Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys

Abstract

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%.

The Use of Neural Networks in the Analysis of Dual Adhesive Single Lap Joints Subjected to Uniaxial Tensile Test

“…The artificial neural network model was created based on analogies to biological counterparts. Neural networks are currently widely used in technical issues, among others [ 27 , 28 , 29 ]. They are a good solution for forecasting and regression problems.…”

Special Issue of the Manufacturing Engineering Society 2020 (SIMES-2020)

“…In machining, Kubo et al [6] use bio-inspired DNA-based computing for determining surface topography of a dressed grinding wheel; Wood et al [7] analyze the machinability of inconel718 alloy with a porous microstructure produced by laser melting powder bed fusion at higher energy densities; and Navarro-Mas et al [8] compare different parameters to evaluate the delamination produces in the edge trimming of basalt fiber reinforced plastics. In forming, Merayo et al [9], in order to characterize the plastic behavior of aluminum alloys, predict the mechanical Among all of them, the topic "Advances and innovations in manufacturing processes" stands out for the number of contributions it has had in this Special Issue (representing 35% of all of them), followed by the topics "Additive manufacturing and 3D printing", "Sustainable and green manufacturing" and "Metrology and quality in manufacturing" (with a 12% each). The rest of the topics represent the remaining 30% of the contributions.…”

“…Regarding the specific contributions of the Special Issue in the journal Materials, 17 contributions about cutting-edge advances in different fields of the manufacturing engineering have been collected. In particular, these concern advances and innovations in manufacturing processes [6][7][8][9][10][11]; additive manufacturing and 3D printing [12,13]; sustainable and green manufacturing [14,15]; metrology and quality in manufacturing [16,17]; manufacturing of new materials [18]; manufacturing systems: machines, equipment and tooling [19]; robotics, mechatronics and manufacturing automation [20]; Industry 4.0 [21]; design, modeling and simulation in manufacturing engineering [22]. Figure 2 shows the main topics and their percentages in this journal.…”

“…Concretely, within the topic "Advances and innovations in manufacturing processes", the works are focused on manufacturing processes. Mainly, machining [6][7][8], forming [9], joining [10] and electroplating [11]. In machining, Kubo et al [6] use bio-inspired DNA-based computing for determining surface topography of a dressed grinding wheel; Wood et al [7] analyze the machinability of inconel718 alloy with a porous microstructure produced by laser melting powder bed fusion at higher energy densities; and Navarro-Mas et al [8] compare different parameters to evaluate the delamination produces in the edge trimming of basalt fiber reinforced plastics.…”

“…In machining, Kubo et al [6] use bio-inspired DNA-based computing for determining surface topography of a dressed grinding wheel; Wood et al [7] analyze the machinability of inconel718 alloy with a porous microstructure produced by laser melting powder bed fusion at higher energy densities; and Navarro-Mas et al [8] compare different parameters to evaluate the delamination produces in the edge trimming of basalt fiber reinforced plastics. In forming, Merayo et al [9], in order to characterize the plastic behavior of aluminum alloys, predict the mechanical properties of the material by artificial neural networks. In joining, Alves et al [10] study attachment of sheets to tube ends made from dissimilar materials with a single stroke.…”