Image-Based Fracture Surface Defect Characterization Methods for Additively Manufactured Ti-6Al-4V Tested in Fatigue

Authors

Austin Ngo, Case Western Reserve University
Kristen Hernandez, Case Western Reserve UniversityFollow
Ayorinde E. Olatunde, Case Western Reserve UniversityFollow
Thomas G. Ciardi, Case Western Reserve UniversityFollow
Alex Harding, Case Western Reserve University
Arafath Nihar, Case Western Reserve UniversityFollow
Anirban Mondal, Case Western Reserve UniversityFollow
Roger H. French, Case Western Reserve UniversityFollow
Laura S. Bruckman, Case Western Reserve UniversityFollow
John J. Lewandowski, Case Western Reserve UniversityFollow

Author ORCID Identifier

Austin Ngo

Kristen J. Hernandez

Ayorinde E. Olatunde

Anirban Mondal

John J. Lewandowski

Roger H. French

Document Type

Article

Publication Date

6-5-2024

Abstract

Fatigue initiation in additively manufactured samples/parts often occurs at processed-induced defects such as lack-of-fusion (LoF), keyhole, or other morphological/microstructural defects that have unique characteristics and measurable qualities. Attempts at identifying and minimizing such defects have utilized optimized processing conditions along with in situ and ex situ characterization that includes metallography and/or X-ray computed tomography (XCT). This paper highlights the benefits of using fracture surface analyses to detect and quantify defects that may not be detected by metallography/XCT due to sectioning and resolution limits. In addition to using manual quantification of fatigue initiating LoF and keyhole defects on fracture surfaces, image-based machine learning using convolutional neural networks such as U-Net were also used to automate the process. Statistical analyses were used to identify the extreme cases of defects that initiated and accelerated fatigue and to model the distribution of defect size and shape characteristics to distinguish the type of defect. Initial results show agreement between trained machine learning models and ground truth data in defect segmentation, and the distributions of defect characteristics are distinguishable to particular process-induced defect types. This article was updated to correct Arafath Nifar to Arafath Nihar in the author list.

Language

English

Publication Title

JOM

Grant

MDS3-COE

Rights

This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.

Recommended Citation

Ngo, A., Hernandez, K., Olatunde, A.E. et al. Image-Based Fracture Surface Defect Characterization Methods for Additively Manufactured Ti-6Al-4V Tested in Fatigue. JOM 76, 4813–4824 (2024). https://doi.org/10.1007/s11837-024-06655-7