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<p>The NiAlCrFeMo high-entropy alloy (HEA) exhibits exceptional high-temperature strength but presents significant machining challenges due to its dual-phase (FCC + BCC) structure. Systematic hot compression tests were conducted over a range of temperatures (1123.15-1323.15&#xa0;K), strain rates (0.1-10&#xa0;s<sup>-1</sup>), and a strain of 0.7. The predictive accuracy of a long short-term memory (LSTM) neural network was compared with two enhanced traditional models: modified Arrhenius and modified Zerilli–Armstrong. The LSTM model achieved superior predictive accuracy with an average absolute relative error (AARE) of 1.79%, a correlation coefficient (R) of 0.999, and a mean absolute error (MAE) of 2.51, significantly outperforming the modified Arrhenius model (AARE: 6.31%, R: 0.992, MAE: 5.90) and the modified Zerilli–Armstrong model (AARE: 8.77%, R: 0.974, MAE: 8.68). The hot working diagram was established based on the predictions from the LSTM model. SEM observations reveal numerous voids in the instability zone, while defect-free morphologies were observed under optimal conditions (1223.15-1323.15&#xa0;K, 0.1&#xa0;s-<sup>1</sup>), confirming the model’s reliability. This work establishes a new paradigm for HEA process optimization by synergizing data-driven and physics-based approaches, offering a robust alternative for precise prediction and enhanced material performance.</p>

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Comparison of Modified Arrhenius, Modified Zerilli–Armstrong, and Long Short-Term Memory Models for Predicting Hot Deformation Behavior of NiAlCrFeMo High-Entropy Alloy

  • 期刊论文

  • 发表日期：

    2025年11月21日

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  • Xinyu Wang,
  • Yuanming Huo,
  • Zhijun Wang,
  • Zhenrong Yan,
  • Wenhan Yu,
  • Kecheng Ma,
  • Hao Chen,
  • Yue Sun,
  • M. James C. Crabbe,
  • Xiaoguang Yue

摘要

The NiAlCrFeMo high-entropy alloy (HEA) exhibits exceptional high-temperature strength but presents significant machining challenges due to its dual-phase (FCC + BCC) structure. Systematic hot compression tests were conducted over a range of temperatures (1123.15-1323.15 K), strain rates (0.1-10 s-1), and a strain of 0.7. The predictive accuracy of a long short-term memory (LSTM) neural network was compared with two enhanced traditional models: modified Arrhenius and modified Zerilli–Armstrong. The LSTM model achieved superior predictive accuracy with an average absolute relative error (AARE) of 1.79%, a correlation coefficient (R) of 0.999, and a mean absolute error (MAE) of 2.51, significantly outperforming the modified Arrhenius model (AARE: 6.31%, R: 0.992, MAE: 5.90) and the modified Zerilli–Armstrong model (AARE: 8.77%, R: 0.974, MAE: 8.68). The hot working diagram was established based on the predictions from the LSTM model. SEM observations reveal numerous voids in the instability zone, while defect-free morphologies were observed under optimal conditions (1223.15-1323.15 K, 0.1 s-1), confirming the model’s reliability. This work establishes a new paradigm for HEA process optimization by synergizing data-driven and physics-based approaches, offering a robust alternative for precise prediction and enhanced material performance.