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用反向优化方法确定40CrMnMoA合金钢的动态再结晶模型参数

Xuewen Chen¹, Qiang Li¹, Bingqi Liu¹

¹School of Materials Science and Engineering, Henan University of Science and Technology, 263 Kaiyuan Avenue, Luoyang 471023, China.

Materials (Basel, Switzerland)

|February 13, 2025

概括

本研究引入了一个反向优化方法,以准确预测热造过程中40CrMnMoA钢的动态再结晶 (DRX). 优化的模型显著提高了预测准确性,使材料微观结构和机械性能能够更好地控制.

科学领域:

材料科学材料科学材料科学
金工业是一种金工业.
计算材料科学科学计算材料科学

背景情况:

在热造过程中,40CrMnMoA钢的微结构决定了其机械性能.
动态再结晶 (DRX) 对于精制谷物结构和增强材料性能至关重要.
在热造过程中准确预测DRX的行为和机械性能对于材料加工至关重要.

研究的目的:

开发和验证一种反向优化方法,以准确确定40CrMnMoA钢的DRX模型参数.
在热造过程中提高DRX体积分数和微观结构演变的预测准确度.
将优化的DRX模型集成到有限元软件中,用于过程模拟.

主要方法:

在40CrMnMoA钢 (9001200 °C,0.0055 s−1) 上进行单轴同热压缩实验.
最初的DRX模型建立使用真实应力-应变数据和传统的平均方法.
使用自适应模拟化 (ASA) 算法对DRX模型参数进行反向优化,最大限度地降低平均平方误差.

主要成果:

优化的DRX模型实现了0.992.99的相关系数 (R).
对于DRX百分比的平均绝对相对误差 (AARE) 和根平均平方误差 (RMSE) 分别减少了34%和2%.
使用优化模型进行的有限元模拟显示,与实际样本相比,颗粒大小的相对误差不到3%.

结论:

反向优化方法准确地识别了40CrMnMoA合金钢的DRX模型参数.
优化的DRX模型显著提高了在热造过程中微结构演变的预测准确性.
这种方法为模拟和优化热造工艺提供了可靠的工具,以改善材料性能.

关键词:

40CrMnMoA合金钢的使用情况适应模拟回火算法适应模拟回火算法动态再结晶的情况.反向优化方法的反向优化方法.

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