Integrating Materials and Manufacturing Innovation最新文献

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

Integrating Materials and Manufacturing Innovation 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

{"title":"New Paradigms in Model Based Materials Definitions for Titanium Alloys in Aerospace Applications","authors":"V. Venkatesh, D. Furrer, S. Burlatsky, M. Kaplan, A. Ross, S. Barker, M. McClure","doi":"10.1007/s40192-024-00373-3","DOIUrl":"https://doi.org/10.1007/s40192-024-00373-3","url":null,"abstract":"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.","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Integrating Materials and Manufacturing Innovation Pub Date : 2024-08-12 DOI: 10.1007/s40192-024-00374-2

Jiale Ma, Wenchao Zhang, Zhiqiang Han, Qingyan Xu, Haidong Zhao

{"title":"An Explainable Deep Learning Model Based on Multi-scale Microstructure Information for Establishing Composition–Microstructure–Property Relationship of Aluminum Alloys","authors":"Jiale Ma, Wenchao Zhang, Zhiqiang Han, Qingyan Xu, Haidong Zhao","doi":"10.1007/s40192-024-00374-2","DOIUrl":"https://doi.org/10.1007/s40192-024-00374-2","url":null,"abstract":"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.","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

Nikhil Prabhu, Martin Diehl

{"title":"Comparison of Full-Field Crystal Plasticity Simulations to Synchrotron Experiments: Detailed Investigation of Mispredictions","authors":"Nikhil Prabhu, Martin Diehl","doi":"10.1007/s40192-024-00359-1","DOIUrl":"https://doi.org/10.1007/s40192-024-00359-1","url":null,"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.","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Integrating Materials and Manufacturing Innovation 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

{"title":"3D Reconstruction of a High-Energy Diffraction Microscopy Sample Using Multi-modal Serial Sectioning with High-Precision EBSD and Surface Profilometry","authors":"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","doi":"10.1007/s40192-024-00370-6","DOIUrl":"https://doi.org/10.1007/s40192-024-00370-6","url":null,"abstract":"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.","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141775532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Integrating Materials and Manufacturing Innovation Pub Date : 2024-07-19 DOI: 10.1007/s40192-024-00362-6

Defne Circi, Ghazal Khalighinejad, Anlan Chen, Bhuwan Dhingra, L. Catherine Brinson

{"title":"How Well Do Large Language Models Understand Tables in Materials Science?","authors":"Defne Circi, Ghazal Khalighinejad, Anlan Chen, Bhuwan Dhingra, L. Catherine Brinson","doi":"10.1007/s40192-024-00362-6","DOIUrl":"https://doi.org/10.1007/s40192-024-00362-6","url":null,"abstract":"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.","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Integrating Materials and Manufacturing Innovation 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

{"title":"L-PBF High-Throughput Data Pipeline Approach for Multi-modal Integration","authors":"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","doi":"10.1007/s40192-024-00368-0","DOIUrl":"https://doi.org/10.1007/s40192-024-00368-0","url":null,"abstract":"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.","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Integrating Materials and Manufacturing Innovation 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

{"title":"Outcomes and Conclusions from the 2022 AM Bench Measurements, Challenge Problems, Modeling Submissions, and Conference","authors":"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","doi":"10.1007/s40192-024-00372-4","DOIUrl":"https://doi.org/10.1007/s40192-024-00372-4","url":null,"abstract":"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.","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning Methods for Microstructural Image Analysis: The State-of-the-Art and Future Perspectives 用于微结构图像分析的深度学习方法：最新技术与未来展望

IF 2.4 3区材料科学

Integrating Materials and Manufacturing Innovation Pub Date : 2024-07-15 DOI: 10.1007/s40192-024-00369-z

Khaled Alrfou, Tian Zhao, Amir Kordijazi

引用次数: 0

Location-Specific Microstructure Characterization Within AM Bench 2022 Nickel Alloy 718 3D Builds 在 2022 型镍合金 718 三维构建的 AM 工作台内进行特定位置的显微结构表征

IF 2.4 3区材料科学

Integrating Materials and Manufacturing Innovation Pub Date : 2024-07-15 DOI: 10.1007/s40192-024-00371-5

L. E. Levine, M. E. Williams, A. Creuziger, M. Stoudt, S. A. Young, K. W. Moon, B. M. Lane

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

Integrating Materials and Manufacturing Innovation 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

引用次数: 0