Mansoor Barati
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Mansoor Barati has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Chad Cathcart
Stelco Inc. Hamilton, Hamilton, ON L8L 8K5, Canada
1 shared publication - Donghui Li
Sustainable Materials Processing Research Group, Department of Materials Science and Engineering, University of Toronto
1 shared publication - Fernando Guerra
Stelco Inc
1 shared publication - Soumitra Kumar Dinda
Process Metallurgy Research Lab, Metallurgical and Materials Engineering, National Institute of Technology, Rourkela, Rourkela, India
1 shared publication
Latest Publications
- Open Access
- 15 December 2024
- Published by Iron and Steel Institute of Japan in ISIJ International
- Vol. 64 (14) , 2031-2041
Sampling Strategy of Bubble Characteristics in a 1:2 Scale Curved Continuous Casting Mold: Parametric and Prediction Study
Argon gas injection in slab continuous casting is common practice to counter SEN clogging phenomena. Bubble characteristics determine the probability of bubble-driven defects such as steel cleanliness, liquid steel reoxidation, and sliver and blister defects. 1:2 scaled water model studies were performed with the help of an advanced high-speed-high-resolution camera shadowgraph imaging technique. Bubble Sauter mean diameter and count were calculated using Trainable Weka segmentation, a machine learning image-based segmentation in the ImageJ platform for different processing conditions such as gas flow rate, liquid flow rate, mold width, and submerged entry nozzle (SEN) depth. A predictive model was developed on the experimental data using an artificial neural network (ANN) algorithm to optimize the bubble mean diameter and count sampling strategy. The model performance is optimized based on the cross-validated adjusted R2. The model shows significant promise with bootstrapping aggregation, five-fold cross-validation, and improved accuracy.
- 15 December 2024
- Published by Iron and Steel Institute of Japan in ISIJ International
- Vol. 64 (14) , 2031-2041
Open AccessSampling Strategy of Bubble Characteristics in a 1:2 Scale Curved Continuous Casting Mold: Parametric and Prediction Study
Argon gas injection in slab continuous casting is common practice to counter SEN clogging phenomena. Bubble characteristics determine the probability of bubble-driven defects such as steel cleanliness, liquid steel reoxidation, and sliver and blister defects. 1:2 scaled water model studies were performed with the help of an advanced high-speed-high-resolution camera shadowgraph imaging technique. Bubble Sauter mean diameter and count were calculated using Trainable Weka segmentation, a machine learning image-based segmentation in the ImageJ platform for different processing conditions such as gas flow rate, liquid flow rate, mold width, and submerged entry nozzle (SEN) depth. A predictive model was developed on the experimental data using an artificial neural network (ANN) algorithm to optimize the bubble mean diameter and count sampling strategy. The model performance is optimized based on the cross-validated adjusted R2. The model shows significant promise with bootstrapping aggregation, five-fold cross-validation, and improved accuracy.