Indirect solution modeling of melting behavior of SiO<sub>2</sub> based on the image processing technology
Bibliographic record
Abstract
Abstract The utilization of tempered blast‐furnace slag through the direct fiber forming process to produce high‐value thermal insulation materials offers a dual benefit: it efficiently utilizes the latent heat in the unused slag and significantly increases the value of blast‐furnace slag utilization. However, measuring the melting properties of iron slag at high temperatures is challenging. In this study, the melting behavior of SiO2 in a high‐temperature molten pool was investigated. We employ dynamic visual data (video stream) captured via a non‐contact charge coupled device video recording system to extract SiO2 contours through image processing. The change in image centroid characteristics is used to establish a convolution function relationship, and MATLAB's traversal search algorithm determines the centroid position of SiO2. Given that SiO2 is proportionate to crucible pixels, the area of the SiO2 is calculated through pixel statistics within these contours. A new indirect method is then proposed to process image information to obtain SiO2 volume and mass at different time points. An exponential fitting yields the melting rate function of SiO2. Finally, this indirect method has been compared with shape from shading, quantitative characterization, and dimensional analysis techniques. Besides, the strengths and limitations of each method have been discussed. Our findings reveal that the indirect solution method presented here boasts straightforward calculation steps and imposes minimal image format requirements, which provides theoretical and technical support for the direct fiber forming process of blast‐furnace slag.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".