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arXiv · 2509.00034

Industrial Steel Slag Flow Data Loading Method for Deep Learning Applications

Mert Sehri·Ana Cardoso·Francisco de Assis Boldt·Patrick Dumond
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Abstract

Steel casting processes are vulnerable to financial losses due to slag flow contamination, making accurate slag flow condition detection essential. This study introduces a novel cross-domain diagnostic method using vibration data collected from an industrial steel foundry to identify various stages of slag flow. A hybrid deep learning model combining one-dimensional convolutional neural networks and long short-term memory layers is implemented, tested, and benchmarked against a standard one-dimensional convolutional neural network. 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 convolutional neural network and long short-term memory architecture, when combined with root mean square preprocessing and a selective embedding data loading strategy, achieves robust classification accuracy, outperforming traditional models and loading techniques. The highest test accuracy of 99.10 +/- 0.30 demonstrates the method's capability for generalization and industrial relevance. This work presents a practical and scalable solution for real-time slag flow monitoring, contributing to improved reliability and operational efficiency in steel manufacturing.

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Mert Sehri, Ana Cardoso, Francisco de Assis Boldt, Patrick Dumond. 2025-08-21. Industrial Steel Slag Flow Data Loading Method for Deep Learning Applications. https://arxiv.org/abs/2509.00034

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