2026-04-30T15:02:09Zhttps://ebiltegia.mondragon.edu/oai/requestoai:ebiltegia.mondragon.edu:20.500.11984/66632024-10-17T06:15:35Zcom_20.500.11984_473col_20.500.11984_478
eBiltegia
author
Duo, Aitor
author
Aguirre, Aitor
other
Fernández López, Patricia
other
Alves, Sofia A.
other
Rogov, Aleksey
other
Yerokhin, Aleksey
other
Quintana, Iban
2024-10-16T13:24:44Z
2024-10-16T13:24:44Z
2024
2079-6412
https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=178050
https://hdl.handle.net/20.500.11984/6663
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.
eng
© 2024 The Authors
plasma electrolytic oxidation (PEO)
Machine learning
prediction models
Alloys
coating
process digitalization
Data-Driven Optimization of Plasma Electrolytic Oxidation (PEO) Coatings with Explainable Artificial Intelligence Insights
http://purl.org/coar/resource_type/c_6501

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http://ebiltegia.mondragon.edu/xmlui/bitstream/20.500.11984/6663/1/Data-Driven%20Optimization%20of%20Plasma%20Electrolytic%20Oxidation%20%28PEO%29%20Coatings%20with%20Explainable%20Artificial%20Intelligence%20Insights.pdf
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Data-Driven Optimization of Plasma Electrolytic Oxidation (PEO) Coatings with Explainable Artificial Intelligence Insights.pdf
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http://ebiltegia.mondragon.edu/xmlui/bitstream/20.500.11984/6663/4/Data-Driven%20Optimization%20of%20Plasma%20Electrolytic%20Oxidation%20%28PEO%29%20Coatings%20with%20Explainable%20Artificial%20Intelligence%20Insights.pdf.txt
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Data-Driven Optimization of Plasma Electrolytic Oxidation (PEO) Coatings with Explainable Artificial Intelligence Insights.pdf.txt