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<p>Fatigue performance is critical to the safe service of 304 austenitic stainless steel components, whereas experimental determination of fatigue strength remains time-consuming and labor-intensive. In this work, three grain-size states with average grain sizes of 22.3, 8.5, and 3.8&#xa0;μm were prepared by cold rolling with reductions of 0%, 30%, and 70%, respectively. The corresponding tensile properties, high-cycle fatigue limits (<i>R</i> =  − 1), and the evolution of deformation substructures and fatigue damage were systematically examined. Grain refinement significantly increased the yield strength and fatigue limit by modifying dislocation arrangements and damage localization behavior. Based on the yield–tensile–fatigue (Y–T–F) framework, a microstructure-sensitive fatigue-strength model was developed by expressing the damage weight factor as a function of grain size and tensile properties. Using fatigue data from 304 stainless steel and various other metallic materials, the model exhibited strong predictive accuracy within the present dataset, with most data falling within ± 10% relative error and all data contained within ± 15%. This model provides a microstructure-guided framework for tailoring the fatigue performance of 304 stainless steel and offers a practical scheme for fatigue-strength prediction in engineering applications.</p>

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A Grain-Size-Dependent High-Cycle Fatigue-Strength Prediction Model for 304 Austenitic Stainless Steel

  • 期刊论文

  • 发表日期：

    2026年3月28日

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  • Hongyan Duan,
  • Jiahui Zhou,
  • Xiao Li,
  • Yuanji Gao,
  • Hongxia Jiang

摘要

Fatigue performance is critical to the safe service of 304 austenitic stainless steel components, whereas experimental determination of fatigue strength remains time-consuming and labor-intensive. In this work, three grain-size states with average grain sizes of 22.3, 8.5, and 3.8 μm were prepared by cold rolling with reductions of 0%, 30%, and 70%, respectively. The corresponding tensile properties, high-cycle fatigue limits (R =  − 1), and the evolution of deformation substructures and fatigue damage were systematically examined. Grain refinement significantly increased the yield strength and fatigue limit by modifying dislocation arrangements and damage localization behavior. Based on the yield–tensile–fatigue (Y–T–F) framework, a microstructure-sensitive fatigue-strength model was developed by expressing the damage weight factor as a function of grain size and tensile properties. Using fatigue data from 304 stainless steel and various other metallic materials, the model exhibited strong predictive accuracy within the present dataset, with most data falling within ± 10% relative error and all data contained within ± 15%. This model provides a microstructure-guided framework for tailoring the fatigue performance of 304 stainless steel and offers a practical scheme for fatigue-strength prediction in engineering applications.