利用自动电子反向散射衍射技术进行高通量微结构表征和工艺相关性分析

IF 2.4 3区 材料科学 Q3 ENGINEERING, MANUFACTURING Integrating Materials and Manufacturing Innovation Pub Date : 2024-06-28 DOI:10.1007/s40192-024-00366-2
J. Elliott Fowler, Timothy J. Ruggles, Dale E. Cillessen, Kyle L. Johnson, Luis J. Jauregui, Robert L. Craig, Nathan R. Bianco, Amelia A. Henriksen, Brad L. Boyce
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引用次数: 0

摘要

优化增材制造(AM)金属和合金加工条件的需求推动了材料性能测量(如拉伸强度或硬度)吞吐能力的进步。由于生产高抛光表面所需的手工和劳动密集型制备方法存在固有瓶颈,AM 金属微观结构的高通量 (HT) 表征大大落后于性能测量的步伐。这种数据吞吐量上的不平等导致人们依赖启发式方法来连接 AM 结构材料的工艺与结构或结构与属性。在本研究中,我们展示了一种实现激光粉末床熔融(LPBF)打印、使用干式电抛光的 HT 制备和 HT 电子反向散射衍射(EBSD)的变革性方法。这种方法被用来构建一个由 600 个实验 EBSD 样品集组成的库,涵盖了 AM Kovar 的各种 LPBF 工艺条件。这个庞大的样本库在参数空间上比大多数先进的研究要宽泛得多,但只需要大约 10 个工时就能获得。我们共享了两个样品设计版本的构建几何图形、表面制备方法和显微镜细节,以及由 600 个 EBSD 数据集组成的整个资料库,目的是让材料界利用这些数据,进一步推动该方法的发展。利用该库,我们研究了工艺与结构之间的关系,发现在使用相同激光参数的情况下,微观结构与构建过程中的位置有意想不到的密切关系。
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High-Throughput Microstructural Characterization and Process Correlation Using Automated Electron Backscatter Diffraction

The need to optimize the processing conditions of additively manufactured (AM) metals and alloys has driven advances in throughput capabilities for material property measurements such as tensile strength or hardness. High-throughput (HT) characterization of AM metal microstructure has fallen significantly behind the pace of property measurements due to intrinsic bottlenecks associated with the artisan and labor-intensive preparation methods required to produce highly polished surfaces. This inequality in data throughput has led to a reliance on heuristics to connect process to structure or structure to properties for AM structural materials. In this study, we show a transformative approach to achieve laser powder bed fusion (LPBF) printing, HT preparation using dry electropolishing and HT electron backscatter diffraction (EBSD). This approach was used to construct a library of > 600 experimental EBSD sample sets spanning a diverse range of LPBF process conditions for AM Kovar. This vast library is far more expansive in parameter space than most state-of-the-art studies, yet it required only approximately 10 labor hours to acquire. Build geometries, surface preparation methods, and microscopy details, as well as the entire library of >600 EBSD data sets over the two sample design versions, have been shared with intent for the materials community to leverage the data and further advance the approach. Using this library, we investigated process–structure relationships and uncovered an unexpected, strong dependence of microstructure on location within the build, when varied, using otherwise identical laser parameters.

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来源期刊
Integrating Materials and Manufacturing Innovation
Integrating Materials and Manufacturing Innovation Engineering-Industrial and Manufacturing Engineering
CiteScore
5.30
自引率
9.10%
发文量
42
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.
期刊最新文献
New Paradigms in Model Based Materials Definitions for Titanium Alloys in Aerospace Applications An Explainable Deep Learning Model Based on Multi-scale Microstructure Information for Establishing Composition–Microstructure–Property Relationship of Aluminum Alloys Comparison of Full-Field Crystal Plasticity Simulations to Synchrotron Experiments: Detailed Investigation of Mispredictions 3D Reconstruction of a High-Energy Diffraction Microscopy Sample Using Multi-modal Serial Sectioning with High-Precision EBSD and Surface Profilometry L-PBF High-Throughput Data Pipeline Approach for Multi-modal Integration
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