Artificial Intelligence in Surface Engineering
DOI:
https://doi.org/10.46842/ipn.cien.v30n2a05Keywords:
Convolutional Neural Networks, Coating Characterization, Artificial Intelligence in Materials ScienceAbstract
The evaluation of surface coatings is essential to ensure the durability and performance of metallic components in industrial applications. This review examines the transformative role of artificial intelligence, particularly Convolutional Neural Networks (CNN) and Vision Transformers (ViT), in automating coating characterization. The integration of these deep learning architectures with digital image processing enables objective, fast, and reproducible analysis, overcoming the subjectivity and time limitations of traditional manual evaluation. The convergence of computer vision, machine learning, and predictive surface engineering models opens up the possibility of simultaneously analyzing microstructural defects, classifying adhesion quality, and optimizing process parameters. This revolutionizes quality control in the coatings industry and facilitates the development of more reliable, high-performance materials.
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Copyright (c) 2026 Alma Alejandra Luna Gómez, Ashley Abril García Garfias, Juan Román Álvarez Meneses, Ernesto David García Bustos, Marco Antonio Doñu Ruiz, Noé López Perrusquia (Autor/a)
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