About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
Use of Computer Vision to Characterize Non-Metallic Inclusions in Steel |
Author(s) |
Nan Gao, Mohammad Abdulsalam, Elizabeth Holm, Bryan Webler |
On-Site Speaker (Planned) |
Bryan Webler |
Abstract Scope |
Non-metallic inclusions are small oxide, sulfide, or nitride particles that have numerous effects on the processing and properties of steel. Inclusions arise during liquid steel processing and their control is an important objective of steel refining. Characterization of inclusions is typically performed with automated scanning electron microscopy plus energy dispersive x-ray spectroscopy (SEM/EDS). In this work we used computer vision and machine learning methods to classify SEM images of inclusions by composition. Use of images alone could reduce the need for EDS measurements of inclusion composition. Results from a random forest algorithm and a convolutional neural network (CNN) were compared. Both methods were able to distinguish images of inclusions from non-inclusions. Prediction accuracy decreased as the number of composition classes were increased. The CNN method exhibited better performance than the random forest method. |
Proceedings Inclusion? |
Undecided |