Data driven performance prediction of titanium-based matrix composites

Abstract

Titanium matrix composites (TMCs) offer superior specific mechanical properties compared to monolithic alloys. However, the complex interdependent effects of composition and processing on the resulting microstructure and properties make experimental determination of optimal TMC formulations challenging. This work explored a materials informatics approach integrating machine learning (ML) modeling with targeted fabrication and characterization for accelerated data-driven design of TMCs. A dataset of 368 data points on composition, processing method and mechanical properties of various TMCs was compiled from literature. Five ML regression algorithms were implemented to predict density, hardness and strength from composition-processing features. Among the models, random forest achieved highest accuracy with R2 scores above 0.93 and low errors. Fabrication of Ti-6Al-4 V/SiC using ML-guided parameters showed excellent agreement between predicted and experimentally measured properties. The ML models outperformed conventional empirical predictions by learning complex structure-property linkages from data. This integrated computational-experimental framework can guide rapid identification of property-optimized TMC formulations by reducing trial-and-error. Further work should focus on physics-based feature engineering and active learning. The data-driven approach demonstrated here shows promise for accelerating development of high-performance TMCs.

Keywords

• Titanium matrix composites
• Machine learning
• Materials informatics
• Data-driven design
• Mechanical properties