Industrial steel slag flow data loading method for deep learning applications
Bibliographic record
Abstract
Steel casting processes are vulnerable to financial losses due to slag flow contamination, making accurate slag flow detection essential for maintaining product quality. This study introduces a novel cross-domain diagnostic approach using vibration data collected from an industrial steel foundry to identify various stages of slag flow. A hybrid deep learning model that integrates one-dimensional convolutional neural networks (CNN) with long short-term memory (LSTM) layers is implemented and tested against a standard CNN architecture. The proposed method processes raw time-domain vibration signals from accelerometers and evaluates performance across 16 distinct domains using a realistic cross-domain dataset split. Results show that the hybrid CNN-LSTM model, when combined with root mean square preprocessing and a selective embedding data loading strategy, demonstrates superior performance compared with tested baselines, achieving a high test accuracy of 99.10 ± 0.30. This indicates strong generalization capability for real-time slag flow monitoring. While the current study is limited to a single foundry dataset, future work will focus on expanding data diversity and incorporating multimodal sensing to further enhance robustness and transferability across industrial environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".