Cargando…
Performance Comparison Between a Statistical Model, a Deterministic Model, and an Artificial Neural Network Model for Predicting Damage From Pitting Corrosion
Various attempts have been made to develop models for predicting the development of damage in metals and alloys due to pitting corrosion. These models may be divided into two classes: the empirical approach which employs extreme value statistics, and the deterministic approach based on perceived mec...
Autores principales: | Urquidi-Macdonald, M., Macdonald, D. D. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
[Gaithersburg, MD] : U.S. Dept. of Commerce, National Institute of Standards and Technology
1994
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8345306/ https://www.ncbi.nlm.nih.gov/pubmed/37405301 http://dx.doi.org/10.6028/jres.099.047 |
Ejemplares similares
-
Studies on Pitting Corrosion of Al-Cu-Li Alloys Part II: Breakdown Potential and Pit Initiation
por: Ghanbari, Elmira, et al.
Publicado: (2019) -
Assessment of Drug Proarrhythmicity Using Artificial Neural Networks With in silico Deterministic Model Outputs
por: Yoo, Yedam, et al.
Publicado: (2021) -
A Computational Pitting Corrosion Model of Magnesium Alloys
por: Chang, Chia-Jung, et al.
Publicado: (2022) -
Studies on Pitting Corrosion of Al–Cu–Li Alloys Part III: Passivation Kinetics of AA2098–T851 Based on the Point Defect Model
por: Ghanbari, Elmira, et al.
Publicado: (2019) -
Modelling of Behavior for Inhibition Corrosion of Bronze Using Artificial Neural Network (ANN)
por: Millán-Ocampo, D. Elusaí, et al.
Publicado: (2018)