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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Machine learning-improved X-CT detection of fatigue critical defects in AM Al alloys

Author(s)

Stefano Beretta, Behnam Salenasab, Daniel Perghem, Shuai Shao, Nima Shamsaei

On-Site Speaker (Planned)

Stefano Beretta

Abstract Scope

With X-ray computed tomography (X-CT) emerging as a state-of-art non-destructive technique for assessing defect contents in AM parts, its reliability in accurately capturing the fatigue critical defects necessitates careful evaluation. In this study, the efficacy of X-CT to estimate the size of fatigue critical defects in AlSi10Mg specimens manufactured by L-PBF is investigated. Results showed that the probability of detection strongly depends on voxel size, defect type and, especially, the segmentation method. The comparison of defects detected at 10- and 20-micron voxel sizes with the ground truth data at 6-micron voxel size showed that LOFs appear as clusters of scattered entities at low resolution and their sizing depends on the ability to recognize such clusters. A machine learning (ML) assisted procedure was developed to recognize and measure clustered entities and was demonstrated to obtain more precise measurements than the current methods based on the interaction of anomalies' stress fields.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Other, Other

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