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Volume 14
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10.3390/coatings14080979
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Article

Data-Driven Optimization of Plasma Electrolytic Oxidation (PEO) Coatings with Explainable Artificial Intelligence Insights

by
Patricia Fernández-López
1,*,
Sofia A. Alves
1,
Aleksey Rogov
2,3,
Aleksey Yerokhin
2,3,
Iban Quintana
1,
Aitor Duo
4 and
Aitor Aguirre-Ortuzar
4
1
Plasma Coatings Technologies Unit, Tekniker, Parke Teknologikoa, C/Iñaki Goenaga, 5, 20600 Eibar, Spain
2
Department of Materials, University of Manchester, Oxford Road, Manchester M12 9PL, UK
3
Henry Royce Institute, University of Manchester, Manchester M12 9PL, UK
4
Faculty of Engineering, Mondragon Unibertsitatea, Loramendi 4, 20500 Arrasate-Mondragon, Spain
*
Author to whom correspondence should be addressed.
Coatings 2024, 14(8), 979; https://doi.org/10.3390/coatings14080979
Submission received: 28 June 2024 / Revised: 24 July 2024 / Accepted: 1 August 2024 / Published: 3 August 2024
(This article belongs to the Special Issue Functional Films/Coatings Processing Technologies: Deposition and Process)
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Abstract

PEO constitutes a promising surface technology for the development of protective and functional ceramic coatings on lightweight alloys. Despite its interesting advantages, including enhanced wear and corrosion resistances and eco-friendliness, the industrial implementation of PEO technology is limited by its relatively high energy consumption. This study explores the development and optimization of novel PEO processes by means of machine learning (ML) to improve the coating thickness. For this purpose, ML models random forest and XGBoost were employed to predict the thickness of the developed PEO coatings based on the key process variables (frequency, current density, and electrolyte composition). The predictive performance was significantly improved by including the composition of the used electrolyte in the models. Furthermore, Shapley values identified the pulse frequency and the TiO2 concentration in the electrolyte as the most influential variables, with higher values leading to increased coating thickness. The residual analysis revealed a certain heteroscedasticity, which suggests the need for additional samples with high thickness to improve the accuracy of the model. This study reveals the potential of artificial intelligence (AI)-driven optimization in PEO processes, which could pave the way for more efficient and cost-effective industrial applications. The findings achieved further emphasize the significance of integrating interactions between variables, such as frequency and TiO2 concentration, into the design of processing operations.
Keywords: plasma electrolytic oxidation (PEO); machine learning; prediction models; cast Al-Si alloys; coating thickness; process digitalization plasma electrolytic oxidation (PEO); machine learning; prediction models; cast Al-Si alloys; coating thickness; process digitalization

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MDPI and ACS Style

Fernández-López, P.; Alves, S.A.; Rogov, A.; Yerokhin, A.; Quintana, I.; Duo, A.; Aguirre-Ortuzar, A. Data-Driven Optimization of Plasma Electrolytic Oxidation (PEO) Coatings with Explainable Artificial Intelligence Insights. Coatings 2024, 14, 979. https://doi.org/10.3390/coatings14080979

AMA Style

Fernández-López P, Alves SA, Rogov A, Yerokhin A, Quintana I, Duo A, Aguirre-Ortuzar A. Data-Driven Optimization of Plasma Electrolytic Oxidation (PEO) Coatings with Explainable Artificial Intelligence Insights. Coatings. 2024; 14(8):979. https://doi.org/10.3390/coatings14080979

Chicago/Turabian Style

Fernández-López, Patricia, Sofia A. Alves, Aleksey Rogov, Aleksey Yerokhin, Iban Quintana, Aitor Duo, and Aitor Aguirre-Ortuzar. 2024. "Data-Driven Optimization of Plasma Electrolytic Oxidation (PEO) Coatings with Explainable Artificial Intelligence Insights" Coatings 14, no. 8: 979. https://doi.org/10.3390/coatings14080979

APA Style

Fernández-López, P., Alves, S. A., Rogov, A., Yerokhin, A., Quintana, I., Duo, A., & Aguirre-Ortuzar, A. (2024). Data-Driven Optimization of Plasma Electrolytic Oxidation (PEO) Coatings with Explainable Artificial Intelligence Insights. Coatings, 14(8), 979. https://doi.org/10.3390/coatings14080979

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