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一种基于数据驱动的方法来研究碳化物对钢加工硬化的影响。

A Data-Driven Approach for Studying the Influence of Carbides on Work Hardening of Steel.

作者信息

Vittorietti Martina, Hidalgo Javier, Galán López Jesús, Sietsma Jilt, Jongbloed Geurt

机构信息

Department of Applied Mathematics, Faculty EWI, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands.

Materials Innovation Institute (M2i), Mekelweg 4, 2628 CD Delft, The Netherlands.

出版信息

Materials (Basel). 2022 Jan 24;15(3):892. doi: 10.3390/ma15030892.

Abstract

This study proposes a new approach to determine phenomenological or physical relations between microstructure features and the mechanical behavior of metals bridging advanced statistics and materials science in a study of the effect of hard precipitates on the hardening of metal alloys. Synthetic microstructures were created using multi-level Voronoi diagrams in order to control microstructure variability and then were used as samples for virtual tensile tests in a full-field crystal plasticity solver. A data-driven model based on Functional Principal Component Analysis (FPCA) was confronted with the classical Voce law for the description of uniaxial tensile curves of synthetic AISI 420 steel microstructures consisting of a ferritic matrix and increasing volume fractions of M23C6 carbides. The parameters of the two models were interpreted in terms of carbide volume fractions and texture using linear mixed-effects models.

摘要

本研究提出了一种新方法,用于确定微观结构特征与金属力学行为之间的唯象学或物理关系,该方法在研究硬质析出相对金属合金硬化作用时,将先进统计学与材料科学联系起来。利用多级Voronoi图创建合成微观结构,以控制微观结构的变异性,然后将其用作全场晶体塑性求解器中虚拟拉伸试验的样本。基于功能主成分分析(FPCA)的数据驱动模型,与经典的Voce定律相对比,用于描述由铁素体基体和体积分数不断增加的M23C6碳化物组成的合成AISI 420钢微观结构的单轴拉伸曲线。使用线性混合效应模型,根据碳化物体积分数和织构对两个模型的参数进行了解释。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/02fa/8839616/c4e6cb93d65e/materials-15-00892-g001.jpg

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本文引用的文献

1
Description of full-range strain hardening behavior of steels.
Springerplus. 2016 Aug 11;5(1):1316. doi: 10.1186/s40064-016-2998-3. eCollection 2016.

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