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<p>The phase transformation kinetics of 35CrMo steel during heating have been harnessed to refine thermo-mechanical processing techniques, thereby ensuring the quality of heat-treated components. To accurately forecast the austenite phase transformation in 35CrMo steel, a non-isothermal diffusion-type Johnson–Mehl–Avrami–Kolmogorov (J–M–A–K) model was developed under continuous heating conditions. The phase transformation activation energy was determined to be <i>Q</i> = 1.097 × 10<sup>6</sup>&#xa0;J/mol, with kinetic parameters <i>n</i> = 0.6434 and <i>k</i><sub>0</sub> = 7.8316 × 10<sup>52</sup>. The adaptive simulated annealing (ASA) algorithm was employed to perform inverse estimation, optimizing the J–M–A–K model parameters to <i>n</i> = 0.6306 and <i>k</i><sub>0</sub> = 1.2 × 10<sup>53</sup>. Taking the cumulative error between the experimental and model values of austenite volume fraction as the objective function, with the minimization of this error as the identification strategy, the optimized model showed an improvement in the prediction correlation coefficient <i>R</i> by 0.285, while the average absolute relative error (AARE) and root-mean-square error (RMSE) decreased by 2.67% and 0.0176, respectively. Through secondary development, the optimized J–M–A–K model was integrated into the SIMHEAT simulation software to simulate the continuous heating of 35CrMo steel. The simulation results correlated highly with experimental data, demonstrating the optimized J–M–A–K model's precision in characterizing the austenitization process of 35CrMo steel during continuous heating.</p>

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Modeling and Inverse Optimization of Austenitization in 35CrMo Steel Using Adaptive Simulated Annealing Algorithm

  • 期刊论文

  • 发表日期：

    2025年3月12日

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  • Junguang He,
  • Zhenfei Cheng,
  • Xuewen Chen,
  • Dongdong Xu,
  • Qinghai Pang,
  • Yihang Li

摘要

The phase transformation kinetics of 35CrMo steel during heating have been harnessed to refine thermo-mechanical processing techniques, thereby ensuring the quality of heat-treated components. To accurately forecast the austenite phase transformation in 35CrMo steel, a non-isothermal diffusion-type Johnson–Mehl–Avrami–Kolmogorov (J–M–A–K) model was developed under continuous heating conditions. The phase transformation activation energy was determined to be Q = 1.097 × 106 J/mol, with kinetic parameters n = 0.6434 and k0 = 7.8316 × 1052. The adaptive simulated annealing (ASA) algorithm was employed to perform inverse estimation, optimizing the J–M–A–K model parameters to n = 0.6306 and k0 = 1.2 × 1053. Taking the cumulative error between the experimental and model values of austenite volume fraction as the objective function, with the minimization of this error as the identification strategy, the optimized model showed an improvement in the prediction correlation coefficient R by 0.285, while the average absolute relative error (AARE) and root-mean-square error (RMSE) decreased by 2.67% and 0.0176, respectively. Through secondary development, the optimized J–M–A–K model was integrated into the SIMHEAT simulation software to simulate the continuous heating of 35CrMo steel. The simulation results correlated highly with experimental data, demonstrating the optimized J–M–A–K model's precision in characterizing the austenitization process of 35CrMo steel during continuous heating.