Abstract
Impact toughness is a critical indicator for the engineering application of titanium alloys, yet it often exhibits a trade-off relationship with strength enhancement. To uncover the dominant factors governing high impact toughness while maintaining strength, a dataset comprising 188 samples of composition, heat treatment processes, and mechanical properties was compiled from publicly available literature. A three-layer ensemble model architecture centered on "expert models" was proposed. This architecture effectively addressed the problem of low accuracy of impact toughness prediction, mostly caused by mixed-type data training, and achieved a coefficient of determination of 0.88. Three dominant factors were identified based on SHAP values, the phase stability ratio (aluminum equivalent/molybdenum equivalent), Mo content, and phase stability ratio's interaction effect with the average atomic radius. Quantitatively, a Mo content trap range (2 – 4) and two optimal design windows for the phase stability ratio and average atomic radius ((2 – 3/1.35 – 1.40) × 10^-10 m, and (0 – 2/1.45 – 1.50) × 10^-10 m) were delineated. Literature validation based on a confusion matrix further verified the clear boundary of these dominant factors.
| Translated title of the contribution | 数据驱动的高强钛合金高冲击韧性主控因素研究 |
|---|---|
| Original language | English |
| Pages (from-to) | 906-917 |
| Number of pages | 12 |
| Journal | Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology |
| Volume | 46 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 2026 |
| Externally published | Yes |
Keywords
- dominant factors
- impact toughness
- machine learning
- titanium alloys
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