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This study presents a predictive computational framework that integrates finite element modeling (FEM) with the Johnson-Mehl-Avrami-Kolmogorov (JMAK) model to investigate the phase transformation behavior in 15-5PH martensitic stainless steel during rapid induction heating. Aimed at achieving localized, intelligent thermal processing, the framework enables precise control and prediction of microstructural evolution under dynamic conditions. Electromagnetic-thermal simulations were conducted using QuickField software to investigate the impact of process parameters, including induction coil position, frequency, current, and voltage, on the temperature distribution within the sample. The resulting thermal fields served as input for a non-isothermal adaptation of the JMAK model to predict precipitation kinetics. Validation was performed using scanning electron microscopy (SEM) and ImageJ image analysis, demonstrating strong agreement between simulated and experimental precipitation distributions. Results revealed that coil misalignment significantly disrupts thermal symmetry, impacting local phase transformation behavior. The proposed FEM–JMAK integration provides a robust and accurate tool for modeling microstructural changes in real-time processing, offering valuable insights for process optimization in aerospace, high-performance steels, and advanced manufacturing applications.

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Computational Modeling of Phase Transformation in 15-5PH Stainless Steel Under Rapid Induction Heating Using FEM–JMAK Framework

  • 会议论文

  • 发表日期：

    2025年

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  • Mahsa Fatemi Mehrabani,
  • Mehrdad Aghaie-Khafri,
  • Mojtaba Esmaeilzadeh

摘要

This study presents a predictive computational framework that integrates finite element modeling (FEM) with the Johnson-Mehl-Avrami-Kolmogorov (JMAK) model to investigate the phase transformation behavior in 15-5PH martensitic stainless steel during rapid induction heating. Aimed at achieving localized, intelligent thermal processing, the framework enables precise control and prediction of microstructural evolution under dynamic conditions. Electromagnetic-thermal simulations were conducted using QuickField software to investigate the impact of process parameters, including induction coil position, frequency, current, and voltage, on the temperature distribution within the sample. The resulting thermal fields served as input for a non-isothermal adaptation of the JMAK model to predict precipitation kinetics. Validation was performed using scanning electron microscopy (SEM) and ImageJ image analysis, demonstrating strong agreement between simulated and experimental precipitation distributions. Results revealed that coil misalignment significantly disrupts thermal symmetry, impacting local phase transformation behavior. The proposed FEM–JMAK integration provides a robust and accurate tool for modeling microstructural changes in real-time processing, offering valuable insights for process optimization in aerospace, high-performance steels, and advanced manufacturing applications.