Abstract
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.
Highlights
Aluminum alloys are among the most widely used materials in the industry, and, their use is still far from being as widespread as that of steel, they have many advantages that make them a very interesting material whose use is growing regularly [1].There is a huge number of aluminum alloys, but few of them are typically used in the industrial field [2], sometimes because it is difficult to find new solutions and, sometimes, because they are special materials with optimized properties to fulfill their requirements, according to their application [3].Aluminum alloys are manufactured by different techniques [4]
This paper studies how to optimize the topology of a multilayer artificial neural network to carry out predictions about mechanical properties of aluminum alloys, such as ultimate tensile strength (UTS), using machine learning
It is a contribution of great industrial interest since it allows exploring how to obtain sufficiently precise estimates with minimal computational cost and, using fewer resources
Summary
Aluminum alloys are among the most widely used materials in the industry, and, their use is still far from being as widespread as that of steel, they have many advantages that make them a very interesting material whose use is growing regularly [1].There is a huge number of aluminum alloys, but few of them are typically used in the industrial field [2], sometimes because it is difficult to find new solutions and, sometimes, because they are special materials with optimized properties to fulfill their requirements, according to their application [3].Aluminum alloys are manufactured by different techniques [4]. There is a huge number of aluminum alloys, but few of them are typically used in the industrial field [2], sometimes because it is difficult to find new solutions and, sometimes, because they are special materials with optimized properties to fulfill their requirements, according to their application [3]. Among all the mechanical properties, the ultimate tensile strength (UTS) plays a key role in the definition of the onset of the plastic instability by tensile tension [3]. This mechanical property is closely related to the strain hardening of the metal and, to its forming capacity under metal forming processes. The plastic deformation is limited by the value of
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