Post-Machining Deformations of Thin-Walled Elements Made of EN AW-2024 T351 Aluminum Alloy as Regards the Mechanical Properties of the Applied, Rolled Semi-Finished Products

The paper presents an evaluation of post-machining deformations of thin-walled elements as regards the mechanical properties of the applied, rolled semi-finished products. Nowadays, wrought aluminum alloys, supplied primarily in the form of rolled plates, are widely applied in the production of thin-walled integral parts. Considering the high requirements for materials, especially in the aviation sector, it is important to be aware of their mechanical properties and for semi-finished products delivered after plastic working to take into account the so-called “technological history” concerning, inter alia, the direction of rolling. The study focused on determining the influence of the ratio of the tension direction to the rolling direction on the selected mechanical properties of the EN AW-2024 T351 aluminum alloy depending on the sample thickness and its relation to the deformation of thin-walled parts. Based on the obtained results, it was found that the sample thickness and the ratio of the tension direction to the rolling direction affected the mechanical properties of the selected aluminum alloy, which in turn translated into post-machining deformations. Summarizing, the textured surface layer had a significant impact on the mentioned deformation. Greater deformations were noted for samples made of a semi-finished product with a thickness of 5 mm in comparison to 12 mm. It was the result of the influence of the surface layer, which at lower thickness had a higher percentage of contents than in thicker samples.

Topological Optimization of Artificial Neural Networks to Estimate Mechanical Properties in Metal Forming Using Machine Learning

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.