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<p>Advanced machine learning approaches have emerged as pivotal methodologies for designing and predicting the performance of high-temperature oxidation-resistant materials. The high-temperature oxidation behavior of the Ti-5Sn-<i>x</i>Er and Ti-6.5Ni-<i>x</i>Er alloys in air was examined using a multi-scale convolutional neural network (MCNN), and the predictions were subsequently validated through experimentation. The neural network exhibited strong predictive accuracy for oxidation behavior at elevated temperatures. The oxidation kinetics of the alloys adhered to a parabolic rate law, and the corresponding oxidation rate constants were quantified. During the oxidation process, the incorporation of Er improved oxidation resistance by inhibiting oxygen diffusion and postponing oxide scale rupture.</p>

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Investigating the High-Temperature Oxidation Behavior of Ti-5Sn-xEr and Ti-6.5Ni-xEr Alloys Using Multi-scale Convolutional Neural Networks

  • 期刊论文

  • 发表日期：

    2026年2月5日

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  • Kangying Li,
  • Yongfa Zheng,
  • Jinming Zhu,
  • Yuanxing Huang,
  • Changsheng Qin,
  • Tonghan Yang

摘要

Advanced machine learning approaches have emerged as pivotal methodologies for designing and predicting the performance of high-temperature oxidation-resistant materials. The high-temperature oxidation behavior of the Ti-5Sn-xEr and Ti-6.5Ni-xEr alloys in air was examined using a multi-scale convolutional neural network (MCNN), and the predictions were subsequently validated through experimentation. The neural network exhibited strong predictive accuracy for oxidation behavior at elevated temperatures. The oxidation kinetics of the alloys adhered to a parabolic rate law, and the corresponding oxidation rate constants were quantified. During the oxidation process, the incorporation of Er improved oxidation resistance by inhibiting oxygen diffusion and postponing oxide scale rupture.