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<p>This study systematically investigates the hot deformation behavior and microstructural evolution of CoNiV medium-entropy alloy (MEA) in the temperature range of 950–1100&#xa0;°C and strain rates of 0.001–1&#xa0;s<sup>−1</sup>. The Arrhenius model and machine learning model were developed to forecast flow stresses at various conditions. The predictive capability of both models was assessed using the coefficients of determination (<i>R</i><sup>2</sup>), average absolute relative error (AARE), and root mean square error (RMSE). The findings show that the osprey optimization algorithm convolutional neural network (OOA-CNN) model outperforms the Arrhenius model, achieving a high <i>R</i><sup>2</sup> value of 0.99959 and lower AARE and RMSE values. The flow stress that the OOA-CNN model predicted was used to generate power dissipation maps and instability maps under different strains. Finally, combining the processing map and microstructure characterization, the ideal processing domain was identified as 1100&#xa0;°C at strain rates of 0.01–0.1&#xa0;s<sup>−1</sup>. This study provided key insights into optimizing the hot working process of CoNiV MEA.</p>

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Hot Deformation Behavior of CoNiV Medium-Entropy Alloy: Constitutive Model, Convolutional Neural Network, Hot Processing Map, and Microstructure Evolution

  • 期刊论文

  • 发表日期：

    2025年6月17日

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  • Biao Zhang,
  • Yuntian Du,
  • Huishuang Jia,
  • Yuanyi Zhou,
  • Liguang Wang,
  • Minghe Zhang,
  • Yunli Feng,
  • Weimin Gao,
  • Ning Xu

摘要

This study systematically investigates the hot deformation behavior and microstructural evolution of CoNiV medium-entropy alloy (MEA) in the temperature range of 950–1100 °C and strain rates of 0.001–1 s−1. The Arrhenius model and machine learning model were developed to forecast flow stresses at various conditions. The predictive capability of both models was assessed using the coefficients of determination (R2), average absolute relative error (AARE), and root mean square error (RMSE). The findings show that the osprey optimization algorithm convolutional neural network (OOA-CNN) model outperforms the Arrhenius model, achieving a high R2 value of 0.99959 and lower AARE and RMSE values. The flow stress that the OOA-CNN model predicted was used to generate power dissipation maps and instability maps under different strains. Finally, combining the processing map and microstructure characterization, the ideal processing domain was identified as 1100 °C at strain rates of 0.01–0.1 s−1. This study provided key insights into optimizing the hot working process of CoNiV MEA.