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Data-Driven Analysis of Dominant Factors for High Impact Toughness in High-Strength Titanium Alloys

  • Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

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 languageEnglish
Pages (from-to)906-917
Number of pages12
JournalBeijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
Volume46
Issue number8
DOIs
Publication statusPublished - 2026
Externally publishedYes

Keywords

  • dominant factors
  • impact toughness
  • machine learning
  • titanium alloys

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