Cast Al-Si alloys, recognized for their excellent mechanical properties, constitute one of the most widely employed non-ferrous substrates in several sectors, and are particularly relevant in the transport industry. Nevertheless, these alloys also display inherent limitations that significantly restrict their use in several applications. Among these limitations, their low hardness, low wear resistance, or limited anti-corrosion properties, which are often not enough when the component is subjected to more severe environments, are particularly relevant. In this context, surface modification and the development of coatings are essential for the application of cast Al-Si alloys. This review focuses on the development of coatings to overcome the complexities associated with improving the performance of cast Al-Si alloys. Against this background, plasma electrolytic oxidation (PEO), an advanced electrochemical treatment that has revolutionized the surface modification of several metallic alloys in recent years, emerges as a promising approach. Despite the growing recognition of PEO technology, the achievement of high-performance coatings on cast Al-Si is still a challenge nowadays, for which reason this review aims to provide an overview of the PEO treatment applied to these alloys. In particular, the impact of the electrolyte chemical composition on the properties of the coatings obtained on different alloys exposed to harsh environments has been analyzed and discussed. By addressing the existing gaps and challenges, this paper contributes to a better understanding of the intricacies associated with the development of robust PEO coatings on cast Al-Si alloys.

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.