Topological Optimization of Artificial Neural Networks to Estimate Mechanical Properties in Metal Forming Using Machine Learning
| Autor: | Ana María Camacho, Alvaro Rodríguez-Prieto, David Merayo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Machine learning computer.software_genre General Materials Science Point (geometry) Topology (chemistry) Mining engineering. Metallurgy Artificial neural network business.industry Metals and Alloys TN1-997 Forming processes metal forming Strain hardening exponent Perceptron topological optimization Characterization (materials science) mechanical property machine learning Metalworking UTS Artificial intelligence aluminum alloy business computer artificial neural network |
| Zdroj: | Metals Volume 11 Issue 8 Metals, Vol 11, Iss 1289, p 1289 (2021) |
| ISSN: | 2075-4701 |
| DOI: | 10.3390/met11081289 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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