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New Paradigms in Model Based Materials Definitions for Titanium Alloys in Aerospace Applications 航空航天应用中基于模型的钛合金材料定义新范例
IF 3.3 3区 材料科学 Q3 ENGINEERING, MANUFACTURING Pub Date : 2024-08-26 DOI: 10.1007/s40192-024-00373-3
V. Venkatesh, D. Furrer, S. Burlatsky, M. Kaplan, A. Ross, S. Barker, M. McClure

To meet the increasing demands of next generation high performance aircraft and propulsion system requirements, multidisciplinary model based materials engineering (MBME) approaches that utilize physics-based, quantitative process–structure–property–performance (PSPP) relationships are being developed and implemented. Traditional empirically based material property development resulted in underutilized component capabilities, and hinder MBME based methods that would allow the optimization of inter-related technologies of materials, manufacturing processes, and component design. A model-based materials engineering framework provides a means to enhanced materials and process definitions, and the rapid development of optimal designs with respect to cost, weight, performance, and qualification. Several key elements have been identified for the successful establishment of a model-based material definition (MBMD) infrastructure. These include individual or sets of specific computational model and data tools that work together in a cross-disciplinary engineering workflow. These infrastructural elements include robust, validated, scalable, fit for purpose models with the appropriate level of accuracy; toolsets for the automated linking of materials, manufacturing, and design models; enhanced data capture and management system to enable model calibration, validation and capture of materials and process variability; and multi-scale materials characterization tools and methods. This paper will review examples of industrial MBMD frameworks for titanium and titanium component design that utilizes validated manufacturing process, microstructure evolution, mechanical property and component/system performance modeling tools that have been developed to support robust PSPP relationships that enable high performance location specific component designs.

为了满足下一代高性能飞机和推进系统日益增长的需求,目前正在开发和实施基于模型的多学科材料工程(MBME)方法,这种方法利用基于物理的定量工艺-结构-性能(PSPP)关系。传统的基于经验的材料属性开发导致组件能力利用不足,并阻碍了基于模型的材料工程方法,而这种方法可以优化材料、制造工艺和组件设计等相互关联的技术。基于模型的材料工程框架为增强材料和工艺定义以及快速开发成本、重量、性能和鉴定方面的最佳设计提供了一种方法。成功建立基于模型的材料定义(MBMD)基础设施的几个关键要素已经确定。其中包括在跨学科工程工作流程中协同工作的单个或成套特定计算模型和数据工具。这些基础设施要素包括:稳健、经过验证、可扩展、适合目的且具有适当精度水平的模型;用于自动连接材料、制造和设计模型的工具集;增强型数据捕获和管理系统,以实现模型校准、验证和捕获材料及工艺变异性;以及多尺度材料表征工具和方法。本文将回顾用于钛和钛组件设计的工业 MBMD 框架示例,这些框架利用经过验证的制造工艺、微观结构演变、机械性能和组件/系统性能建模工具来支持稳健的 PSPP 关系,从而实现高性能的特定位置组件设计。
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引用次数: 0
An Explainable Deep Learning Model Based on Multi-scale Microstructure Information for Establishing Composition–Microstructure–Property Relationship of Aluminum Alloys 基于多尺度微观结构信息的可解释深度学习模型,用于建立铝合金的成分-微观结构-性能关系
IF 3.3 3区 材料科学 Q3 ENGINEERING, MANUFACTURING Pub Date : 2024-08-12 DOI: 10.1007/s40192-024-00374-2
Jiale Ma, Wenchao Zhang, Zhiqiang Han, Qingyan Xu, Haidong Zhao

Establishing a quantitative composition–microstructure–property relationship is crucial in material design and process optimization. With the advent of big data technology, deep learning models, as a machine learning method that can automatically extract information from images, have been widely used in microstructure image identification and property prediction. However, most deep learning models only use single-scale images for property prediction, ignoring the multi-scale microstructure information of materials. In this study, an explainable deep learning model was developed based on a multi-modal and multi-scale dataset for predicting the tensile properties of aluminum alloys. Three different kinds of aluminum alloys, each incorporating various trace elements, were prepared to evaluate the adaptation of the model. The predicted results demonstrate that the integration of multi-scale microstructure information significantly improves the model’s prediction ability. Furthermore, the intrinsic mechanisms of the deep learning model were elucidated through the application of a visualization technique, greatly improving the explicability of the model. In addition, the effect of data redundancy on model performance was analyzed. The proposed deep learning model breaks the traditional deep learning strategy with the single-scale image as input and effectively establishes the composition–microstructure–property relationship.

建立定量的成分-微观结构-性能关系对于材料设计和工艺优化至关重要。随着大数据技术的发展,深度学习模型作为一种能自动从图像中提取信息的机器学习方法,已被广泛应用于微观结构图像识别和性能预测。然而,大多数深度学习模型仅使用单尺度图像进行性能预测,忽略了材料的多尺度微观结构信息。本研究基于多模态和多尺度数据集开发了一种可解释的深度学习模型,用于预测铝合金的拉伸性能。为了评估模型的适应性,研究人员制备了三种不同类型的铝合金,每种合金都含有不同的微量元素。预测结果表明,整合多尺度微观结构信息可显著提高模型的预测能力。此外,通过应用可视化技术阐明了深度学习模型的内在机制,大大提高了模型的可解释性。此外,还分析了数据冗余对模型性能的影响。所提出的深度学习模型打破了以单比例图像为输入的传统深度学习策略,有效地建立了成分-微结构-属性关系。
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引用次数: 0
Comparison of Full-Field Crystal Plasticity Simulations to Synchrotron Experiments: Detailed Investigation of Mispredictions 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
IF 3.3 3区 材料科学 Q3 ENGINEERING, MANUFACTURING Pub Date : 2024-08-06 DOI: 10.1007/s40192-024-00359-1
Nikhil Prabhu, Martin Diehl

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.

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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引用次数: 0
3D Reconstruction of a High-Energy Diffraction Microscopy Sample Using Multi-modal Serial Sectioning with High-Precision EBSD and Surface Profilometry 利用多模态序列切片与高精度 EBSD 和表面轮廓测量法重建高能衍射显微镜样品的三维结构
IF 3.3 3区 材料科学 Q3 ENGINEERING, MANUFACTURING Pub Date : 2024-07-24 DOI: 10.1007/s40192-024-00370-6
Gregory Sparks, Simon A. Mason, Michael G. Chapman, Jun-Sang Park, Hemant Sharma, Peter Kenesei, Stephen R. Niezgoda, Michael J. Mills, Michael D. Uchic, Paul A. Shade, Mark Obstalecki

High-energy diffraction microscopy (HEDM) combined with in situ mechanical testing is a powerful nondestructive technique for tracking the evolving microstructure within polycrystalline materials during deformation. This technique relies on a sophisticated analysis of X-ray diffraction patterns to produce a three-dimensional reconstruction of grains and other microstructural features within the interrogated volume. However, it is known that HEDM can fail to identify certain microstructural features, particularly smaller grains or twinned regions. Characterization of the identical sample volume using high-resolution surface-specific techniques, particularly electron backscatter diffraction (EBSD), can not only provide additional microstructure information about the interrogated volume but also highlight opportunities for improvement of the HEDM reconstruction algorithms. In this study, a sample fabricated from undeformed “low solvus, high refractory” nickel-based superalloy was scanned using HEDM. The volume interrogated by HEDM was then carefully characterized using a combination of surface-specific techniques, including epi-illumination optical microscopy, zero-tilt secondary and backscattered electron imaging, scanning white light interferometry, and high-precision EBSD. Custom data fusion protocols were developed to integrate and align the microstructure maps captured by these surface-specific techniques and HEDM. The raw and processed data from HEDM and serial sectioning have been made available via the Materials Data Facility (MDF) at https://doi.org/10.18126/4y0p-v604 for further investigation.

高能衍射显微镜(HEDM)与原位机械测试相结合,是一种强大的无损技术,可用于跟踪多晶材料在变形过程中不断演变的微观结构。该技术依赖于对 X 射线衍射图样的复杂分析,以生成晶粒的三维重建图,以及所检测体积内的其他微观结构特征。然而,众所周知,HEDM 可能无法识别某些微观结构特征,尤其是较小的晶粒或孪晶区域。使用高分辨率表面特异性技术,特别是电子反向散射衍射(EBSD)对相同的样品体积进行表征,不仅可以提供有关询问体积的更多微观结构信息,还能突出改进 HEDM 重建算法的机会。在本研究中,使用 HEDM 扫描了由未变形的 "低溶解度、高耐火度 "镍基超级合金制成的样品。然后,利用外延照明光学显微镜、零倾斜二次电子和背散射电子成像、扫描白光干涉仪和高精度 EBSD 等表面特定技术的组合,对 HEDM 扫描的体积进行了仔细的表征。开发了定制数据融合协议,以整合和校准这些特定表面技术和 HEDM 采集的微观结构图。HEDM 和序列切片的原始数据和处理数据已通过 https://doi.org/10.18126/4y0p-v604 的材料数据设施(MDF)提供,供进一步研究使用。
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引用次数: 0
How Well Do Large Language Models Understand Tables in Materials Science? 大型语言模型如何理解材料科学中的表格?
IF 3.3 3区 材料科学 Q3 ENGINEERING, MANUFACTURING Pub Date : 2024-07-19 DOI: 10.1007/s40192-024-00362-6
Defne Circi, Ghazal Khalighinejad, Anlan Chen, Bhuwan Dhingra, L. Catherine Brinson

Advances in materials science require leveraging past findings and data from the vast published literature. While some materials data repositories are being built, they typically rely on newly created data in narrow domains because extracting detailed data and metadata from the enormous wealth of publications is immensely challenging. The advent of large language models (LLMs) presents a new opportunity to rapidly and accurately extract data and insights from the published literature and transform it into structured data formats for easy query and reuse. In this paper, we build on initial strategies for using LLMs for rapid and autonomous data extraction from materials science articles in a format curatable by materials databases. We presented the subdomain of polymer composites as our example use case and demonstrated the success and challenges of LLMs on extracting tabular data. We explored different table representations for use with LLMs, finding that a multimodal model with an image input yielded the most promising results. This model achieved an accuracy score of 0.910 for composition information extraction and an F(_1) score of 0.863 for property name information extraction. With the most conservative evaluation for the property extraction requiring exact match in all the details, we obtained an F(_1) score of 0.419. We observed that by allowing varying degrees of flexibility in the evaluation, the score can increase to 0.769. We envision that the results and analysis from this study will promote further research directions in developing information extraction strategies from materials information sources.

材料科学的进步需要利用过去从大量出版文献中获得的发现和数据。虽然目前正在建立一些材料数据资源库,但它们通常依赖于狭窄领域的新创建数据,因为从大量出版物中提取详细数据和元数据是一项巨大的挑战。大型语言模型(LLM)的出现提供了一个新的机会,可以快速、准确地从已发表的文献中提取数据和见解,并将其转换为结构化数据格式,以便于查询和重用。在本文中,我们在使用 LLMs 从材料科学文章中快速、自主地提取数据的初步策略基础上,将其转化为材料数据库可处理的格式。我们以聚合物复合材料子领域为例,展示了 LLMs 在提取表格数据方面的成功经验和面临的挑战。我们探索了与 LLM 配合使用的不同表格表示法,结果发现,使用图像输入的多模态模型取得了最理想的结果。该模型在成分信息提取方面的准确率达到了 0.910,在属性名称信息提取方面的准确率达到了 0.863。在要求所有细节完全匹配的最保守的属性提取评估中,我们得到的 F(_1) 分数为 0.419。我们观察到,如果在评估中允许不同程度的灵活性,得分可以提高到 0.769。我们希望本研究的结果和分析能进一步推动从材料信息源中开发信息提取策略的研究方向。
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引用次数: 0
L-PBF High-Throughput Data Pipeline Approach for Multi-modal Integration 用于多模式集成的 L-PBF 高通量数据管道方法
IF 3.3 3区 材料科学 Q3 ENGINEERING, MANUFACTURING Pub Date : 2024-07-19 DOI: 10.1007/s40192-024-00368-0
Kristen J. Hernandez, Thomas G. Ciardi, Rachel Yamamoto, Mingjian Lu, Arafath Nihar, Jayvic Cristian Jimenez, Pawan K. Tripathi, Brian Giera, Jean-Baptiste Forien, John J. Lewandowski, Roger H. French, Laura S. Bruckman

Metal-based additive manufacturing requires active monitoring solutions for assessing part quality. Multiple sensors and data streams, however, generate large heterogeneous data sets that are impractical for manual assessment and characterization. In this work, an automated pipeline is developed that enables feature extraction from high-speed camera video and multi-modal data analysis. The framework removes the need for manual assessment through the utilization of deep learning techniques and training models in a weakly supervised paradigm. We demonstrate this pipeline’s capability over 700,000 high-speed camera frames. The pipeline successfully extracts melt pool and spatter geometries and links them to corresponding pyrometry, radiography, and processparameter information. 715 individual prints are examined to reveal melt pool areas that exceeds 0.07 mm2 and pyrometry signal over a threshold (375 pyrometry units) were more likely to have defects. These automated processes enable massive throughput of characterization techniques.

基于金属的快速成型制造需要主动监测解决方案来评估零件质量。然而,多个传感器和数据流会产生大量异构数据集,人工评估和表征不切实际。在这项工作中,开发了一个自动管道,可从高速摄像视频和多模态数据分析中提取特征。该框架通过利用深度学习技术和弱监督范式中的训练模型,消除了人工评估的需要。我们在 700,000 个高速摄像帧上演示了这一管道的能力。该管道成功提取了熔池和喷溅几何图形,并将它们与相应的高温测量、射线照相和工艺参数信息联系起来。对 715 个印花进行检查,发现熔池面积超过 0.07 平方毫米和高温测量信号超过临界值(375 个高温测量单位)的印花更有可能存在缺陷。这些自动化流程实现了表征技术的高吞吐量。
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引用次数: 0
Outcomes and Conclusions from the 2022 AM Bench Measurements, Challenge Problems, Modeling Submissions, and Conference 2022 年调幅工作台测量、挑战问题、模型提交和会议的成果与结论
IF 3.3 3区 材料科学 Q3 ENGINEERING, MANUFACTURING Pub Date : 2024-07-17 DOI: 10.1007/s40192-024-00372-4
Lyle Levine, Brandon Lane, Chandler Becker, James Belak, Robert Carson, David Deisenroth, Edward Glaessgen, Thomas Gnaupel-Herold, Michael Gorelik, Gretchen Greene, Saadi Habib, Callie Higgins, Michael Hill, Nik Hrabe, Jason Killgore, Jai Won Kim, Gerard Lemson, Kalman Migler, Shawn Moylan, Darren Pagan, Thien Phan, Maxwell Praniewicz, David Rowenhorst, Edwin Schwalbach, Jonathan Seppala, Brian Simonds, Mark Stoudt, Jordan Weaver, Ho Yeung, Fan Zhang

The Additive Manufacturing Benchmark Test Series (AM Bench) provides rigorous measurement data for validating additive manufacturing (AM) simulations for a broad range of AM technologies and material systems. AM Bench includes extensive in situ and ex situ measurements, simulation challenges for the AM modeling community, and a corresponding conference series. In 2022, the second round of AM Bench measurements, challenge problems, and conference were completed, focusing primarily upon laser powder bed fusion (LPBF) processing of metals, and both material extrusion processing and vat photopolymerization of polymers. In all, more than 100 people from 10 National Institute of Standards and Technology (NIST) divisions and 21 additional organizations were directly involved in the AM Bench 2022 measurements, data management, and conference organization. The international AM community submitted 138 sets of blind modeling simulations for comparison with the in situ and ex situ measurements, up from 46 submissions for the first round of AM Bench in 2018. Analysis of these submissions provides valuable insight into current AM modeling capabilities. The AM Bench data are permanently archived and freely accessible online. The AM Bench conference also hosted an embedded workshop on qualification and certification of AM materials and components.

增材制造基准测试系列(AM Bench)为验证各种增材制造(AM)技术和材料系统的增材制造(AM)模拟提供了严格的测量数据。AM Bench 包括广泛的原位和非原位测量、针对 AM 建模社区的模拟挑战以及相应的系列会议。2022 年,第二轮 AM Bench 测量、挑战问题和会议已经完成,主要侧重于金属的激光粉末床熔融 (LPBF) 加工以及聚合物的材料挤压加工和大桶光聚合。来自美国国家标准与技术研究院(NIST)10 个部门和另外 21 个组织的 100 多人直接参与了 AM Bench 2022 的测量、数据管理和会议组织工作。国际AM界提交了138套盲法建模模拟,用于与原位和离场测量进行比较,比2018年第一轮AM Bench提交的46套有所增加。对这些提交数据的分析为了解当前的 AM 建模能力提供了宝贵的信息。AM Bench 数据永久存档,可免费在线访问。AM Bench 会议还举办了关于 AM 材料和组件的资格认证和认证的嵌入式研讨会。
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引用次数: 0
Correlative X-ray Computed Tomography and Optical Microscopy Serial Sectioning Data of Additive Manufactured Ti-6Al-4V 添加剂制造的 Ti-6Al-4V 的相关 X 射线计算机断层扫描和光学显微镜序列切片数据
IF 3.3 3区 材料科学 Q3 ENGINEERING, MANUFACTURING Pub Date : 2024-07-10 DOI: 10.1007/s40192-024-00367-1
Bryce R. Jolley, Daniel M. Sparkman, Michael G. Chapman, Edwin J. Schwalbach, Michael D. Uchic

An additively manufactured titanium alloy sample has been characterized by X-ray computed tomography and optical microscopy serial sectioning to enable a correlative analysis of internal porosity. Titanium alloy ball bearings were adhered to the surface of the cylindrical sample to aid the registration of the datasets. The characterization data includes five X-ray computed tomography scans from four different instruments and optical microscopy serial sectioning images. The methods and parameters used for collecting these multiple datasets, and reconstructed data for each dataset‘s selected volume of interest are provided. Raw projection data from each computed tomography scan are also offered. Unanticipated artifacts within the serial sectioning experiment are highlighted, and the potential impact of these artifacts is discussed.

通过 X 射线计算机断层扫描和光学显微镜连续切片对添加制造的钛合金样品进行了表征,以便对内部孔隙率进行相关分析。钛合金球轴承被粘附在圆柱形样品的表面,以帮助数据集的登记。表征数据包括来自四种不同仪器的五次 X 射线计算机断层扫描和光学显微镜连续切片图像。本文提供了收集这些多个数据集的方法和参数,以及每个数据集所选感兴趣体积的重建数据。此外,还提供了每次计算机断层扫描的原始投影数据。重点介绍了序列切片实验中的意外伪影,并讨论了这些伪影的潜在影响。
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引用次数: 0
Tackling Structured Knowledge Extraction from Polymer Nanocomposite Literature as an NER/RE Task with seq2seq 利用 seq2seq 将聚合物纳米复合材料文献中的结构化知识提取作为 NER/RE 任务来处理
IF 3.3 3区 材料科学 Q3 ENGINEERING, MANUFACTURING Pub Date : 2024-07-01 DOI: 10.1007/s40192-024-00363-5
Bingyin Hu, Anqi Lin, L. Catherine Brinson

There is an urgent need for ready access to published data for advances in materials design, and natural language processing (NLP) techniques offer a promising solution for extracting relevant information from scientific publications. In this paper, we present a domain-specific approach utilizing a Transformer-based model, T5, to automate the generation of sample lists in the field of polymer nanocomposites (PNCs). Leveraging large-scale corpora, we employ advanced NLP techniques including named entity recognition and relation extraction to accurately extract sample codes, compositions, group references, and properties from PNC papers. The T5 model demonstrates competitive performance in relation extraction using a TANL framework and an EM-style input sequence. Furthermore, we explore multi-task learning and joint-entity-relation extraction to enhance efficiency and address deployment concerns. Our proposed methodology, from corpora generation to model training, showcases the potential of structured knowledge extraction from publications in PNC research and beyond.

材料设计的进步迫切需要随时获取已发表的数据,而自然语言处理(NLP)技术为从科学出版物中提取相关信息提供了一种前景广阔的解决方案。在本文中,我们提出了一种特定领域的方法,利用基于 Transformer 的模型 T5 自动生成聚合物纳米复合材料 (PNC) 领域的样本列表。利用大规模语料库,我们采用了先进的 NLP 技术(包括命名实体识别和关系提取),从 PNC 论文中准确提取样本代码、组成、组参考和属性。使用 TANL 框架和 EM 式输入序列,T5 模型在关系提取方面表现出了极强的竞争力。此外,我们还探索了多任务学习和联合实体关系提取,以提高效率并解决部署问题。我们提出的方法,从语料生成到模型训练,展示了从 PNC 研究及其他领域的出版物中进行结构化知识提取的潜力。
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引用次数: 0
High-Throughput Microstructural Characterization and Process Correlation Using Automated Electron Backscatter Diffraction 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 3.3 3区 材料科学 Q3 ENGINEERING, MANUFACTURING 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

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.

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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引用次数: 0
期刊
Integrating Materials and Manufacturing Innovation
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