Application of machine learning in predicting properties of steel materials

Predicting the properties of steel materials is crucial for optimizing production processes and enhancing product quality. In recent years, Machine Learning (ML) has demonstrated significant advantages in this field due to its powerful capabilities in data mining and pattern recognition. This paper provides a systematic review of the current applications and challenges of machine learning in predicting steel material properties, with a focus on analyzing research progress and industrial application cases of algorithms such as Backpropagation Neural Networks (BPNN), Support Vector Machines (SVM), Random Forests (RF), and deep learning. Key challenges including data quality, model generalizability, and interpretability are discussed. Furthermore, future innovative directions are outlined, such as the application of Large Language Models (LLM), multi-modal data fusion, and mechanism model deep integration, aiming to offer guidance and technical references for the intelligent transformation of the steel industry.