@misc{10498/35028,
year = {2020},
url = {http://hdl.handle.net/10498/35028},
abstract = {El acero inoxidable es un material empleado en numerosas aplicaciones de la Ingeniería Civil. Por esta razón, es fundamental asegurar la durabilidad de dicho material en las distintas aplicaciones que se pueden encontrar dentro de este sector. En este trabajo, se evalúa el comportamiento del acero inoxidable EN 1.4404 mediante un modelo basado en redes neuronales artificiales. El objetivo es poder predecir su comportamiento frente a la corrosión bajo determinadas condiciones medioambientales. El modelo basado en Redes Neuronales Artificiales consta de tres etapas. Los resultados obtenidos resultan satisfactorios y demuestran que el modelo desarrollado se puede emplear para predecir el estado de corrosión por picadura del material de forma automática con una elevada especificidad y sensibilidad.},
abstract = {Stainless steel has proved to be an important material to be used in a wide range of applications. For this reason, ensuring the durability of this alloy is essen tial. In this work, pitting corrosion behaviour of EN 1.4404 stainless steel is evaluated in marine environment in order to develop a model capable of predicting its pitting
corrosion status by an automatic way. Although electro chemical techniques and microscopic analysis have been shown to be very useful tools for corrosion studies, these techniques may present some limitationus. With the aim to solve these drawbacks, a three-step model based on Arti ficial Neural Networks (ANNs) is proposed. The results reveal that the model can be used to predict pitting corrosion status of this alloy with satisfactory sensitivity and specificity with no need to resort to electrochemical tests or microscopic analysis. Therefore, the proposed model
becomes a useful tool to predict the behaviour of the material against pitting corrosion in saline environment automatically.},
publisher = {De Gruyter},
keywords = {corrosion},
keywords = {electrochemistry},
keywords = {modelling},
keywords = {stainless steel},
title = {The use of artificial neural networks for modelling pitting corrosion behaviour of EN 1.4404 stainless steel in marine environment: data analysis and new developments},
doi = {10.1515/corrrev-2019-0095},
author = {Jiménez Come, María Jesús and Martín Rodríguez, María de la Luz and Matres Serrano, Victoria and Mena Baladés, Jesús Daniel},
}