Optimizing parameter selection for machine learning models to predict coating characteristics in cold spray processes on polymeric materials
Alessia Serena PERNA, Alessia AURIEMMA CITARELLA, Fabiola DE MARCO, Luigi DI BIASI, Massimo DURANTE, Genoveffa TORTORA, Antonio VISCUSI
Abstract. Cold spray (CS) is an advanced manufacturing technique used to deposit metallic layers onto various materials. It works by accelerating metallic particles to supersonic speeds with pressurized gas, enabling adhesion through mechanical interlocking upon impact with the substrate. The integration of Artificial Intelligence (AI) can significantly improve process understanding and enhance the quality of this additive manufacturing method. This work employs Finite Element Method simulations combined with an additional parameter, density, to improve the predictive accuracy of the cold spray process. To ensure consistency and comparability, Z-normalization was applied to standardize the data. Among the tested approaches, the best results for flattening and penetration depth output parameters (RMSE of 0.26 and 0.27, respectively) are achieved by Gaussian Process Regression model with a Matern 5/2 kernel applying the Z-normalization.
Keywords
Machine Learning, Cold Spray, Neural Networks, Thermal Spray
Published online 5/7/2025, 10 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Alessia Serena PERNA, Alessia AURIEMMA CITARELLA, Fabiola DE MARCO, Luigi DI BIASI, Massimo DURANTE, Genoveffa TORTORA, Antonio VISCUSI, Optimizing parameter selection for machine learning models to predict coating characteristics in cold spray processes on polymeric materials, Materials Research Proceedings, Vol. 54, pp 2058-2067, 2025
DOI: https://doi.org/10.21741/9781644903599-221
The article was published as article 221 of the book Material Forming
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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