| About this Abstract |
| Meeting |
MS&T23: Materials Science & Technology
|
| Symposium
|
Materials Informatics for Images and Multi-dimensional Datasets
|
| Presentation Title |
Machine Learning Segmentation Methods for Fatigue Fracture Surface Defect Analyses |
| Author(s) |
Austin Q. Ngo, Oluwatumininu Adeeko, David Scannapieco, John J. Lewandowski |
| On-Site Speaker (Planned) |
Oluwatumininu Adeeko |
| Abstract Scope |
Machine learning algorithms for feature segmentation were utilized to characterize fracture surfaces of Ti-6Al-4V fatigue samples fabricated by powder bed additive manufacturing. Process-induced defects were identified and quantified by feature training an image classification system using SEM images of fatigue fracture surfaces. Process defects were found to range in size, shape, and population due to variations in AM build process parameters. All process-induced defects across each fracture surface were quantified, with ‘killer’ fatigue crack initiating defects being identified. The fracture surface defect characteristics are compared to the corresponding S-N fatigue data for defect-based fatigue life modeling in a fatigue initiating defect-based Kitagawa-Murakami-type approach. In addition, this computer vision method is compared to ground truth manual quantification of fracture surface defects. The advantages and challenges of implementing ML algorithms to streamline fracture surface defect quantification will be discussed. |