Peripheral coarse grain prediction in extruded AA6082: Combining finite element simulations with neural networks
Marco Negozio, Adrian H.A. Lutey, Antonio Segatori, Riccardo Pelaccia, Sara Di Donato, Barbara Reggiani, Lorenzo Donati
Abstract. Peripheral Coarse Grain (PCG) is a critical defect that affects the mechanical and crash performance of extruded AA6XXX aluminum profiles, particularly in automotive applications. Traditional methods to address this issue rely on extensive experimental campaigns, which are resource-intensive and often lead to conservative process parameters, reducing production efficiency. This study develops and validates a predictive model for PCG formation, combining finite element method (FEM) simulations and machine learning (ML) techniques. Data from FEM simulations and experiments were used to train and test a model employing artificial neural networks (ANNs) for PCG prediction. The proposed approach enables accurate PCG forecasting, providing a robust tool for optimizing process parameters, reducing reliance on empirical methods and advancing smart manufacturing solutions.
Keywords
Aluminum Alloy Extrusion, Finite Element Simulation, Artificial Neural Network, Peripheral Coarse Grain, Microstructure Prediction
Published online 9/10/2025, 8 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Marco Negozio, Adrian H.A. Lutey, Antonio Segatori, Riccardo Pelaccia, Sara Di Donato, Barbara Reggiani, Lorenzo Donati, Peripheral coarse grain prediction in extruded AA6082: Combining finite element simulations with neural networks, Materials Research Proceedings, Vol. 57, pp 344-351, 2025
DOI: https://doi.org/10.21741/9781644903735-40
The article was published as article 40 of the book Italian Manufacturing Association Conference
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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