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<p>Austenitic heat-resistant steels (AHSs) are widely used in high-temperature components, and their oxidation resistance at high temperature is closely related to long-term service reliability. However, high-temperature oxidation is an intrinsically complex, nonlinear, strongly time-dependent, and multi-stage process, making accurate prediction of oxidation kinetics challenging. In this work, an attention-enhanced bidirectional long short-term memory (Bi-LSTM) framework is proposed to predict oxidation mass gain of AHSs in dry air. The model integrates static compositional descriptors with dynamic time-series inputs, enabling the extraction of sequence-dependent features, while an attention mechanism adaptively emphasizes critical temporal information. The developed model achieves high predictive accuracy in a representative run (<i>R</i>² = 0.9524), outperforming traditional machine learning models and the baseline LSTM model. The framework provides a practical data-driven sequence modeling method for long-term lifetime assessment and design of AHSs, and offers a transferable methodological basis for other alloy systems with complex time-varying processes.</p>

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Deep learning-based time-series model for predicting high-temperature oxidation behavior in austenitic heat-resistant steels

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月1日

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  • Zhuopeng Li,
  • Dexin Zhu,
  • Tongbo Jiang,
  • Hong-Hui Wu,
  • Chunlei Shang,
  • Shuize Wang,
  • Junheng Gao,
  • Haitao Zhao,
  • Chaolei Zhang,
  • Yuhe Huang,
  • Jun Lu,
  • Xinping Mao

摘要

Austenitic heat-resistant steels (AHSs) are widely used in high-temperature components, and their oxidation resistance at high temperature is closely related to long-term service reliability. However, high-temperature oxidation is an intrinsically complex, nonlinear, strongly time-dependent, and multi-stage process, making accurate prediction of oxidation kinetics challenging. In this work, an attention-enhanced bidirectional long short-term memory (Bi-LSTM) framework is proposed to predict oxidation mass gain of AHSs in dry air. The model integrates static compositional descriptors with dynamic time-series inputs, enabling the extraction of sequence-dependent features, while an attention mechanism adaptively emphasizes critical temporal information. The developed model achieves high predictive accuracy in a representative run (R² = 0.9524), outperforming traditional machine learning models and the baseline LSTM model. The framework provides a practical data-driven sequence modeling method for long-term lifetime assessment and design of AHSs, and offers a transferable methodological basis for other alloy systems with complex time-varying processes.