Comparison of Full-Field Crystal Plasticity Simulations to Synchrotron Experiments: Detailed Investigation of Mispredictions

IF 2.4 3区材料科学 Q3 ENGINEERING, MANUFACTURING

Integrating Materials and Manufacturing Innovation Pub Date : 2024-08-06 DOI:10.1007/s40192-024-00359-1

Nikhil Prabhu, Martin Diehl

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Abstract

Crystal plasticity-based digital twins are an alternative to expensive and time-consuming experiments for the investigation of micro-mechanical material behavior. However, before using simulations as an alternative for experiments, the capabilities and limitations of the modeling approach need to be known. This is best done by juxtaposing the predictions of digital twins against experimental data. The present work assesses the capabilities of full-field crystal plasticity simulations in an additively manufactured (AM) nickel-based superalloy that was characterized in situ by high-energy X-ray diffraction microscopy and electron backscatter diffraction as part of challenge 4 of air force research laboratory’s AM modeling challenge series. To ensure that the grains of interest are initialized with the measured eigenstrains, a novel scheme is proposed and its performance is evaluated. The overall agreement between simulation and experiment is assessed and compared to previous studies using the same dataset and aspects for which a systematic disagreement is seen are discussed.

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全场晶体塑性模拟与同步加速器实验的比较：错误预测的详细调查

基于晶体塑性的数字孪晶是研究微观机械材料行为的昂贵而耗时的实验的替代方案。然而，在使用模拟替代实验之前，需要了解建模方法的能力和局限性。最好的办法是将数字孪生预测与实验数据进行对比。本研究评估了全场晶体塑性模拟在加法制造（AM）镍基超合金中的能力，该超合金是通过高能 X 射线衍射显微镜和电子反向散射衍射进行现场表征的，是空军研究实验室 AM 建模挑战系列赛挑战 4 的一部分。为确保相关晶粒根据测量的特征应变进行初始化，提出了一种新方案并对其性能进行了评估。评估了模拟与实验之间的整体一致性，并与之前使用相同数据集进行的研究进行了比较，讨论了存在系统性分歧的方面。

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来源期刊

Integrating Materials and Manufacturing Innovation Engineering-Industrial and Manufacturing Engineering

CiteScore

5.30

自引率

9.10%

发文量

审稿时长

39 days

期刊介绍： The journal will publish: Research that supports building a model-based definition of materials and processes that is compatible with model-based engineering design processes and multidisciplinary design optimization; Descriptions of novel experimental or computational tools or data analysis techniques, and their application, that are to be used for ICME; Best practices in verification and validation of computational tools, sensitivity analysis, uncertainty quantification, and data management, as well as standards and protocols for software integration and exchange of data; In-depth descriptions of data, databases, and database tools; Detailed case studies on efforts, and their impact, that integrate experiment and computation to solve an enduring engineering problem in materials and manufacturing.