Research Progress of Machine Learning Aided Titanium Alloys Design

Titanium alloys, known for their excellent mechanical properties, biocompatibility, corrosion resistance, and heat resistance, have become the material of choice for high-performance structural components. They are widely used in fields such as medical devices, chemical engineering, aerospace, and naval ships. As the variety of alloying elements in titanium alloys continues to increase, the mapping relationship between composition, processing, and performance becomes increasingly complex. Traditional design methods for titanium alloys, such as molybdenum equivalence, d-electron alloy theory, and valence electron concentration, struggle to accurately capture the complex interactions between alloying elements and their impact on the microstructure and performance. In recent years, machine learning technologies have shown promise in uncove-ring hidden relationships between material composition, processing, microstructure, and performance by mining material data through algorithms. This offers the potential to optimize experimental processes and overcome the high cost and inefficiency of trial-and-error methods in material de-sign, opening new avenues for intelligent design of titanium alloys. This paper presents an overview of the machine learning-assisted design process for titanium alloys, including data sourcing and preprocessing, feature engineering, machine learning modeling and prediction, and optimization design. It reviews the research progress of data-driven intelligent design paradigms in titanium alloy development. Finally, the paper analyzes the challenges faced by this new research paradigm in the titanium alloy field and discusses its future prospects.