2024
DOI: 10.1149/1945-7111/ad1e3c
|View full text |Cite
|
Sign up to set email alerts
|

Accelerating FEM-Based Corrosion Predictions Using Machine Learning

David Montes de Oca Zapiain,
Demitri Maestas,
Matthew Roop
et al.

Abstract: Atmospheric corrosion of metallic parts is a widespread materials degradation phenomena that is challenging to predict given its dependence on many factors (e.g. environmental, physiochemical, and part geometry). For materials with long expected service lives, accurately predicting the degree to which corrosion will degrade part performance is especially difficult due to the stochastic nature of corrosion damage spread across years or decades of service. The finite element method (FEM) is a computational tech… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
3

Relationship

1
2

Authors

Journals

citations
Cited by 3 publications
references
References 59 publications
0
0
0
Order By: Relevance