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Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Advanced Real Time Imaging
Presentation Title Combination of confocal laser scanning microscopy and machine learning model for the prediction of oxide dissolution in the steelmaking slag
Author(s) Chunguang Shen, Changji Xuan, Wangzhong Mu
On-Site Speaker (Planned) Wangzhong Mu
Abstract Scope Dissolution of non-metallic inclusion in the steelmaking slag is important for cleanliness control of steel product. With the development of high-temperature confocal laser scanning microscope (HT-CLSM), new insights have been obtained due to its in-situ image observation characteristics, higher resolution and precise process control. However, HT-CLSM for this measurement has limitations, e.g., selecting slag composition, including transition metal oxide. In addition, it is time-consuming and challenging to succeed for every measurement. Machine learning (ML) is the key enabling technology for material science and industry digitalization. The database for ML model is collected using almost all available HT-CLSM experimental data, and subsequently established database can be trained by the ensemble learning method. Al2O3 dissolution is the primary process to be predicted in the current study, but all the other kinds of oxide dissolution, e.g., MgO, SiO2 and MgAl2O4, are included and tested in the model.
Proceedings Inclusion? Planned:
Keywords Iron and Steel, Characterization, Pyrometallurgy

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