A knowledge graph attention network for the cold-start problem in intelligent manufacturing: Interpretability and accuracy improvement

In the rolling production of steel, predicting the performance of new products is challenging due to the low variety of data distributions resulting from standardized manufacturing processes and fixed product categories. This scenario poses a significant hurdle for machine learning models, leading to what is commonly known as the “cold-start problem”. To address this issue, we propose a knowledge graph attention neural network for steel manufacturing (SteelKGAT). By leveraging expert knowledge and a multi-head attention mechanism, SteelKGAT aims to enhance prediction accuracy. Our experimental results demonstrate that the SteelKGAT model outperforms existing methods when generalizing to previously unseen products. Only the SteelKGAT model accurately captures the feature trend, thereby offering correct guidance in product tuning, which is of practical significance for new product development (NPD). Additionally, we employ the Integrated Gradients (IG) method to shed light on the model's predictions, revealing the relative importance of each feature within the knowledge graph. Notably, this work represents the first application of knowledge graph attention neural networks to address the cold-start problem in steel rolling production. By combining domain expertise and interpretable predictions, our knowledge-informed SteelKGAT model provides accurate insights into the mechanical properties of products even in cold-start scenarios.