Author ORCID Identifier
Document Type
Poster
Publication Date
Summer 6-18-2024
Abstract
Fatigue and fracture studies focused on process defects that occur in Additive Manufacturing (AM) materials have shown that defect populations possess features which are better measured with extreme value statistics (EVS). In AM alloys, defect occurrences increase with material volume. This situation facilitates the need to model process defects in the path of fatigue crack growth with suitable statistical tools, such as EVS, which is more cost-effective when compared to destructive experiments. The application of EVS on defect space features helps determine the difference in defects present on fracture surfaces. As the fatigue quality of any material depends on its extreme value flaws, we use the Block Maxima and Peak over Threshold methodologies to study the distribution of the features of the defects in AM Ti-6Al-4V and make recommendations for the distributions of best fit based on different scenarios with different ranges of complexity of different defect types.
Keywords
extreme value statistics (EVS), fatigue process defects
Publication Title
The 2nd World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2024)
Rights
© The Author(s)
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Recommended Citation
Olatunde, Ayorinde E.; Hernandez, Kristen; Ngo, Austin; Nihar, Arafath; Ciardi, Thomas G.; Yamamoto, Rachel; Tripathi, Pawan K.; French, Roger H.; Lewandowski, John J.; and Mondal, Anirban, "Extreme Value Statistics Analysis of Process Defects in Additive Manufacturing Materials" (2024). Faculty Scholarship. 347.
https://commons.case.edu/facultyworks/347