The fatigue response of Additive Manufacturing (AM) components is driven by manufacturing defects - whose size mainly depends on process parameters - and by the resulting microstructure - mainly affected by heat treatments and process parameters. In the paper, Machine Learning (ML) algorithms are applied to estimate the fatigue response from AM process parameters and heat treatment properties. Feed-forward neural networks (FFNN) and physics-informed neural network (PINN) algorithms are designed and validated on literature datasets of AM AlSi10Mg alloy, proving the effectiveness of physics-based ML approaches in predicting the fatigue response of AM parts. Leveraging PINN interpretability, the authors analyse the relationship between process parameters and fatigue response.

Data driven method for predicting the effect of process parameters on the fatigue response of additive manufactured AlSi10Mg parts / Ciampaglia, A.; Tridello, A.; Paolino, D. S.; Berto, Filippo. - In: INTERNATIONAL JOURNAL OF FATIGUE. - ISSN 0142-1123. - 170:(2023). [10.1016/j.ijfatigue.2023.107500]

Data driven method for predicting the effect of process parameters on the fatigue response of additive manufactured AlSi10Mg parts

Berto Filippo
Ultimo
Conceptualization
2023

Abstract

The fatigue response of Additive Manufacturing (AM) components is driven by manufacturing defects - whose size mainly depends on process parameters - and by the resulting microstructure - mainly affected by heat treatments and process parameters. In the paper, Machine Learning (ML) algorithms are applied to estimate the fatigue response from AM process parameters and heat treatment properties. Feed-forward neural networks (FFNN) and physics-informed neural network (PINN) algorithms are designed and validated on literature datasets of AM AlSi10Mg alloy, proving the effectiveness of physics-based ML approaches in predicting the fatigue response of AM parts. Leveraging PINN interpretability, the authors analyse the relationship between process parameters and fatigue response.
2023
additive manufacturing; fatigue; Machine Learning; neural networks; physic informed
01 Pubblicazione su rivista::01a Articolo in rivista
Data driven method for predicting the effect of process parameters on the fatigue response of additive manufactured AlSi10Mg parts / Ciampaglia, A.; Tridello, A.; Paolino, D. S.; Berto, Filippo. - In: INTERNATIONAL JOURNAL OF FATIGUE. - ISSN 0142-1123. - 170:(2023). [10.1016/j.ijfatigue.2023.107500]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1668641
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