Fe-11Al-5Mn-1Nb-1C低密度钢热变形行为的高级建模和显微组织研究

IF 4 3区 材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY
Bashista Kumar Mahanta, Pankaj Rawat, Sumit Bhan, Swagata Roy
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引用次数: 0

摘要

采用GLEEBLE 3800R热模拟装置,研究了Fe-11Al-5Mn-1Nb-1C低密度钢在900 ~ 1200℃、应变速率1 ~ 0.001 s−1范围内的热变形行为。建立了arrhenius型本构模型和基于人工神经网络(ANN)方法的双层进化神经网络(EvoNN)模型,用于预测变形过程中的流动应力。EvoNN模型的预测精度高于本构模型。显微组织分析表明,在900℃和1000℃时,合金为铁素体基体,次生相为碳化物kappa;在1100℃和1200℃时,合金为铁素体+奥氏体的双相结构,相界面处有细小的kappa碳化物。在所有的热压缩样品中均一致存在NbC颗粒。900℃和1000℃时发生部分动态再结晶(DRX), 1100℃和1200℃时发生更广泛的动态再结晶。在较低应变速率下晶粒粗化明显,随着应变速率的降低晶粒粗化程度增大。细小的NbC颗粒和碳化物固定了晶界,可能会延迟DRX的发生,而粗的NbC颗粒似乎会增强粒子刺激成核(PSN),给DRX动力学带来复杂性,并导致本构模型和EvoNN模型的差异。图形抽象
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advanced Modeling and Microstructural Insights into the Hot Deformation Behavior of Fe–11Al–5Mn–1Nb–1C Low-Density Steel

The hot deformation behavior of Fe–11Al–5Mn–1Nb–1C low-density steel was investigated using a GLEEBLE 3800R thermomechanical simulator across a temperature range of 900–1200 ℃ and strain rates of 1–0.001 s−1. An Arrhenius-type constitutive model was developed to predict flow stress during deformation, alongside a bilayer evolutionary neural network (EvoNN) model based on an artificial neural network (ANN) approach. The EvoNN model demonstrated higher prediction accuracy than the constitutive model. Microstructural analysis revealed a ferritic matrix with kappa carbide as a secondary phase at 900 and 1000 ℃, while at 1100 and 1200 ℃, a dual-phase structure (ferrite + austenite) with fine kappa carbides at the phase interface was observed. NbC particles were consistently present in all hot compressed samples. Partial dynamic recrystallization (DRX) occurred at 900 and 1000 ℃, whereas more extensive DRX was observed at 1100 and 1200 ℃. Grain coarsening was evident at lower strain rates, increasing as the strain rate decreased. Fine NbC particles and kappa carbides pinned grain boundaries, potentially delaying DRX onset, while coarse NbC particles appeared to enhance particle-stimulated nucleation (PSN), introducing complexity to DRX dynamics and contributing to model discrepancies in the constitutive and EvoNN model.

Graphical Abstract

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来源期刊
Metals and Materials International
Metals and Materials International 工程技术-材料科学:综合
CiteScore
7.10
自引率
8.60%
发文量
197
审稿时长
3.7 months
期刊介绍: Metals and Materials International publishes original papers and occasional critical reviews on all aspects of research and technology in materials engineering: physical metallurgy, materials science, and processing of metals and other materials. Emphasis is placed on those aspects of the science of materials that are concerned with the relationships among the processing, structure and properties (mechanical, chemical, electrical, electrochemical, magnetic and optical) of materials. Aspects of processing include the melting, casting, and fabrication with the thermodynamics, kinetics and modeling.
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