| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
2025 Technical Division Student Poster Contest
|
| Presentation Title |
SPG-49: Artificial Intelligence Enabled Microstructural Feature Detection |
| Author(s) |
Hariharan Chithalamangalam Saravanan, Pooja Maurya, Alex Gaudio, Asim Smailagic, P. Chris Pistorius |
| On-Site Speaker (Planned) |
Pooja Maurya |
| Abstract Scope |
Binder jet printing is gaining potential for a large-scale production on an economic scale. Debinding and sintering are the major post-processing steps. Defects like oxide inclusions, porosity and carbides incorporated in the sintered part can deteriorate its mechanical properties. Images of the heat treated and etched sintered samples captured by optical microscopy reveal various features, one of which is the carbides precipitated along the grain boundaries. An image processing pipeline is proposed to isolate carbide pixels from non-carbide pixels (grain boundary with no carbides, pores, etch pits, and twin boundaries) in microscopy images. Different pre-trained deep networks are utilized in the main pipeline stages: an edge detection model selects candidate carbide pixels from a high-resolution input image; a blob detector removes pore artifacts from candidate pixels; and a line detector removes the twin boundaries from the remaining candidate pixels. |
| Proceedings Inclusion? |
Undecided |
| Keywords |
Machine Learning, Additive Manufacturing, Iron and Steel |