|About this Abstract
||MS&T22: Materials Science & Technology
||Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
||Machine Learning Segmentation Methods for Fatigue Fracture Surface Defect Analyses
||Austin Q. Ngo, David Scannapieco, Shuheng Zhang, Shuyue Bian, Collin Sharpe, John Lewandowski
|On-Site Speaker (Planned)
||Austin Q. Ngo
Machine learning algorithms for feature segmentation were utilized on fracture surfaces of LPBF fatigue samples. Process-induced defects were identified and quantified by 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 LPBF build process parameters. Different types of process defects (i.e. lack of fusion, keyhole) were found to be more prevalent based on particular process parameter sets. 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 Kitagawa-Murakami-type approach. In addition, this computer vision method is compared to manual identification and quantification of fracture surface defects. The advantages of implementing ML algorithms to streamline fracture surface defect quantification will be discussed.