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Integrated design framework for titanium aluminides through interpretable machine learning
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AI
DOI：10.1016/j.jallcom.2025.184937.png)
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Abstract
En 中文
Ti-Al based alloys are high temperature structural materials used in extreme aerospace applications, such as jet engine blades. However, conventional discovery of new alloy compositions with superior properties has been slow and resource-intensive. To accelerate this, a novel interpretable machine learning (ML) framework was developed to identify novel alloy compositions with promising properties. While our integrated ML framework builds upon prior work in materials design, its novelty lies in the systematic application to titanium aluminide alloys and the specific use of explainable AI (XAI) techniques, particularly Shapley Additive exPlanations (SHAP), to interpret feature influence and guide the definition of the search space for one-shot multi-property Bayesian optimization (MPBO). This framework also encompasses comprehensive data collection from literature, an ML-based imputation strategy to handle data sparsity, and robust multi-property regression algorithms. Alloy C-I (Ti- 49.4Al- 3.5Cr- 2.9Nb) and Alloy C-II (Ti- 47.2Al- 3.8Cr- 3Nb) were predicted using this framework. Validation experiments show that these compositions demonstrated superior room-temperature yield and tensile strengths in both tension and compression tests, and also superior hardness, compared to a reference Ti-4822 alloy prepared under identical laboratory conditions. Thus, our approach advances the development of highperformance titanium alloys and exemplifies the integration of ML into materials discovery.
Keywords:
Titanium aluminides
Multi-property Bayesian optimization
Interpretable Machine learning
Inverse design
Journal
IF:
6.3
Papers:
8.3W
Citations:
24.3W
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