Industrial big data analysis strategy based on automatic data classification and interpretable knowledge graph

Machine learning has emerged as a critical tool for processing the complex and large-scale datasets generated in the steel industry. However, a single machine learning model struggles to capture all relevant information owing to the variety of steel grades, thereby limiting its extensibility and broader industrial application. Furthermore, most machine-learning models are “black boxes” with low interpretability. Therefore, this paper proposes a novel strategy for industrial big data analysis. First, a data classification model was developed using unsupervised clustering techniques to automatically divide the dataset into four distinct classes. Simultaneously, key physical metallurgy (PM) variables were calculated and incorporated as input features to improve property prediction. Next, an interpretable knowledge graph was constructed for each class, connecting the relevant features with the PM variables. Using these graphs, a graph convolutional network (GCN) model was developed for each class to predict the steel properties. The results demonstrate that this approach delivers better predictions than models without automatic data classification. Furthermore, compared to traditional deep learning models, GCN models based on interpretable knowledge graphs provide superior prediction accuracy and significantly improved interpretability and extensibility.