Industrial Steel Slag Flow Data Loading Method for Deep Learning Applications

TL;DR

This paper presents a hybrid deep learning approach using vibration data for real-time slag flow detection in steel casting, achieving high accuracy and robustness across multiple industrial domains.

Contribution

It introduces a novel cross-domain diagnostic method with a hybrid CNN-LSTM model and a new data loading strategy for slag flow condition detection.

Findings

Achieved 99.10% test accuracy in slag flow classification.

Outperformed traditional CNN models and loading techniques.

Demonstrated robustness across 16 industrial domains.

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.