Integrating Materials and Manufacturing Innovation最新文献_第4页

A Methodology for the Rapid Qualification of Additively Manufactured Materials Based on Pore Defect Structures 基于孔隙缺陷结构的快速鉴定快速成型材料的方法学

IF 3.3 3区材料科学

Integrating Materials and Manufacturing Innovation Pub Date : 2024-02-27 DOI: 10.1007/s40192-024-00343-9

Krzysztof S. Stopka, Andrew Desrosiers, Amber Andreaco, Michael D. Sangid

{"title":"A Methodology for the Rapid Qualification of Additively Manufactured Materials Based on Pore Defect Structures","authors":"Krzysztof S. Stopka, Andrew Desrosiers, Amber Andreaco, Michael D. Sangid","doi":"10.1007/s40192-024-00343-9","DOIUrl":"https://doi.org/10.1007/s40192-024-00343-9","url":null,"abstract":"Additive manufacturing (AM) can create net or near-net-shaped components while simultaneously building the material microstructure, therefore closely coupling forming the material and shaping the part in contrast to traditional manufacturing with distinction between the two processes. While there are well-heralded benefits to AM, the widespread adoption of AM in fatigue-limited applications is hindered by defects such as porosity resulting from off-nominal process conditions. The vast number of AM process parameters and conditions make it challenging to capture variability in porosity that drives fatigue design allowables during qualification. Furthermore, geometric features such as overhangs and thin walls influence local heat conductivity and thereby impact local defects and microstructure. Consequently, qualifying AM material within parts in terms of material properties is not always a straightforward task. This article presents an approach for rapid qualification of AM fatigue-limited parts and includes three main aspects: (1) seeding pore defects of specific size, distribution, and morphology into AM specimens, (2) combining non-destructive and destructive techniques for material characterization and mechanical fatigue testing, and (3) conducting microstructure-based simulations of fatigue behavior resulting from specific pore defect and microstructure combinations. The proposed approach enables simulated data to be generated to validate and/or augment experimental fatigue data sets with the intent to reduce the number of tests needed and promote a more rapid route to AM material qualification. Additionally, this work suggests a closer coupling between material qualification and part certification for determining material properties at distinct regions within an AM part.","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140001588","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

A General Materials Data Science Framework for Quantitative 2D Analysis of Particle Growth from Image Sequences 从图像序列对粒子生长进行二维定量分析的通用材料数据科学框架

IF 3.3 3区材料科学

Integrating Materials and Manufacturing Innovation Pub Date : 2024-02-20 DOI: 10.1007/s40192-024-00342-w

Sameera Nalin Venkat, Thomas G. Ciardi, Mingjian Lu, Preston C. DeLeo, Jube Augustino, Adam Goodman, Jayvic Cristian Jimenez, Anirban Mondal, Frank Ernst, Christine A. Orme, Yinghui Wu, Roger H. French, Laura S. Bruckman

{"title":"A General Materials Data Science Framework for Quantitative 2D Analysis of Particle Growth from Image Sequences","authors":"Sameera Nalin Venkat, Thomas G. Ciardi, Mingjian Lu, Preston C. DeLeo, Jube Augustino, Adam Goodman, Jayvic Cristian Jimenez, Anirban Mondal, Frank Ernst, Christine A. Orme, Yinghui Wu, Roger H. French, Laura S. Bruckman","doi":"10.1007/s40192-024-00342-w","DOIUrl":"https://doi.org/10.1007/s40192-024-00342-w","url":null,"abstract":"Phase transformations are a challenging problem in materials science, which lead to changes in properties and may impact performance of material systems in various applications. We introduce a general framework for the analysis of particle growth kinetics by utilizing concepts from machine learning and graph theory. As a model system, we use image sequences of atomic force microscopy showing the crystallization of an amorphous fluoroelastomer film. To identify crystalline particles in an amorphous matrix and track the temporal evolution of the particle dispersion, we have developed quantitative methods of 2D analysis. 700 image sequences were analyzed using a neural network architecture, achieving 0.97 pixel-wise classification accuracy as a measure of the correctly classified pixels. The growth kinetics of isolated and impinged particles were tracked throughout time using these image sequences. The relationship between image sequences and spatiotemporal graph representations was explored to identify the proximity of crystallites from each other. The framework enables the analysis of all image sequences without the requirement of sampling for specific particles or timesteps for various materials systems.","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139918420","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

MICRO2D: A Large, Statistically Diverse, Heterogeneous Microstructure Dataset MICRO2D：统计多样的大型异构微结构数据集

IF 3.3 3区材料科学

Integrating Materials and Manufacturing Innovation Pub Date : 2024-02-12 DOI: 10.1007/s40192-023-00340-4

Andreas E. Robertson, Adam P. Generale, Conlain Kelly, Michael O. Buzzy, Surya R. Kalidindi

{"title":"MICRO2D: A Large, Statistically Diverse, Heterogeneous Microstructure Dataset","authors":"Andreas E. Robertson, Adam P. Generale, Conlain Kelly, Michael O. Buzzy, Surya R. Kalidindi","doi":"10.1007/s40192-023-00340-4","DOIUrl":"https://doi.org/10.1007/s40192-023-00340-4","url":null,"abstract":"The availability of large, diverse datasets has enabled transformative advances in a wide variety of technical fields by unlocking data scientific and machine learning techniques. In Materials Informatics for Heterogeneous Microstructures capitalization on these techniques has been limited due to the extreme complexity of generating or curating sizeable heterogeneous microstructure datasets. Historically, this difficulty can be attributed to two main hurdles: quantification (i.e., measuring microstructure diversity) and curation (i.e., generating diverse microstructures). In this paper, we present a framework for curating large, statistically diverse mesoscale microstructure datasets composed of 2-phase microstructures. The framework generates microstructures which are statistically diverse with respect to their n-point statistics—the primary emphasis is on diversity in their 2-point statistics. The framework’s foundation is a proposed set of algorithms for synthesizing salient 2-point statistics and neighborhood distributions. We generate statistically diverse microstructures by using the outputs of these algorithms as inputs to a statistically conditioned Local-Global Decomposition generation procedure. Finally, we demonstrate the proposed framework by curating MICRO2D, a diverse, large-scale, and open source heterogeneous microstructure dataset comprised of 87, 379 2-phase microstructures. The contained microstructures are periodic and (256 times 256) pixels. The dataset also contains salient homogenized elastic and thermal properties computed across a range of constituent contrast ratios for each microstructure. Using MICRO2D, we analyze the statistical and property diversity achievable via the proposed framework. We conclude by discussing important areas of future research in microstructure dataset curation.","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139767431","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

Temperature-Dependent Material Property Databases for Marine Steels—Part 6: HY-100 海洋用钢随温度变化的材料特性数据库--第 6 部分：HY-100

IF 3.3 3区材料科学

Integrating Materials and Manufacturing Innovation Pub Date : 2024-02-07 DOI: 10.1007/s40192-023-00339-x

{"title":"Temperature-Dependent Material Property Databases for Marine Steels—Part 6: HY-100","authors":"","doi":"10.1007/s40192-023-00339-x","DOIUrl":"https://doi.org/10.1007/s40192-023-00339-x","url":null,"abstract":"<h3>Abstract</h3> Integrated Computational Materials Engineering (ICME)-based tools and techniques have been identified as the best path forward for distortion mitigation in thin-plate steel construction at shipyards. ICME tools require temperature-dependent material properties—including specific heat, thermal conductivity, coefficient of thermal expansion, elastic modulus, yield strength, flow stress, and microstructural evolution—to achieve accurate computational results for distortion and residual stress. However, the required temperature-dependent material property databases of U.S. Navy-relevant steels are not available in the literature. Therefore, a comprehensive testing plan for some of the most common marine steels used in the construction of U.S. Naval vessels was completed. This testing plan included DH36, HSLA-65, HSLA-80, HSLA-100, HY-80, and HY-100 steel with a nominal thickness of 4.76 mm (3/16-in.). This report is the sixth part of a seven-part series detailing the pedigreed steel data. The first six reports will report the material properties for each of the individual steel grades, whereas the final report will compare and contrast the measured steel properties across all six steels. This report will focus specifically on the data associated with HY-100 steel.","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139767638","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

A Common Data Dictionary and Common Data Model for Additive Manufacturing 用于增材制造的通用数据字典和通用数据模型

IF 3.3 3区材料科学

Integrating Materials and Manufacturing Innovation Pub Date : 2024-02-07 DOI: 10.1007/s40192-024-00341-x

Alexander Kuan, Kareem S. Aggour, Shengyen Li, Yan Lu, Luke Mohr, Alex Kitt, Hunter Macdonald

{"title":"A Common Data Dictionary and Common Data Model for Additive Manufacturing","authors":"Alexander Kuan, Kareem S. Aggour, Shengyen Li, Yan Lu, Luke Mohr, Alex Kitt, Hunter Macdonald","doi":"10.1007/s40192-024-00341-x","DOIUrl":"https://doi.org/10.1007/s40192-024-00341-x","url":null,"abstract":"Additive manufacturing (AM) leverages emerging technologies and well-adopted processes to produce near-net-shape products. The advancement of AM technology requires data management tools to collect, store, and share information through the product development lifecycle and across the material and machine value chain. To address the need for sharing data among AM developers and practitioners, an AM common data dictionary (AM-CDD) was first developed based on community consensus to provide a common lexicon for AM, and later standardized by ASTM International. Following the AM-CDD work, the development of a common data model (AM-CDM) defining the structure and relationships of the key concepts, and terms in the AM-CDD is being developed. These efforts have greatly facilitated system integrations and AM data exchanges among various organizations. This work outlines the effort to create the AM-CDD and AM-CDM, with a focus on the design of the AM-CDM. Two use cases are provided to demonstrate the adoption of these efforts and the interoperability enabled by the AM-CDM for different engineering applications managed by different types of database technology. In these case studies, the AM-CDM is implemented in two distinct formats to curate AM data from NIST—the first in XML from their additive manufacturing material database and the second in OWL from their 2022 AM bench database. These use cases present the power of the AM-CDM for data representation, querying, and seamless data exchange. Our implementation experiences and some challenges are highlighted that can assist others in future adoptions of the AM-CDM for data integration and data exchange applications.","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139767447","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

Blockchain-Based Security Access Control System for Sharing Squeeze Casting Process Database 基于区块链的挤压铸造工艺数据库共享安全访问控制系统

IF 3.3 3区材料科学

Integrating Materials and Manufacturing Innovation Pub Date : 2024-02-06 DOI: 10.1007/s40192-023-00337-z

Jianxin Deng, Gang Liu, Xiangming Zeng

{"title":"Blockchain-Based Security Access Control System for Sharing Squeeze Casting Process Database","authors":"Jianxin Deng, Gang Liu, Xiangming Zeng","doi":"10.1007/s40192-023-00337-z","DOIUrl":"https://doi.org/10.1007/s40192-023-00337-z","url":null,"abstract":"Presently, material databases construction is a trending topic. We propose to adopt a collaborative and shared model to accelerate building a squeeze casting process database. To achieve co-construction and sharing of the databases, ensure the reliability of data, database operation security, and on-demand access control of data, a secure access control system has been established for squeeze casting process databases based on blockchain technology. The system saves the database data on a local server, implements automatic access control for users through smart contracts, stores user operation records on the blockchain, and ensures that the data is modifiable while the user operation records cannot be tampered with. Because of the inadequate security of traditional transaction processes where data is transmitted as source data, we use asymmetric encryption algorithm to encrypt the source data and transmit ciphertext to improve data sharing security. The system has been developed and implemented, and the security verification experiment has demonstrated the feasibility and effectiveness of the design.","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139767912","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

Phase Composition and Phase Transformation of Additively Manufactured Nickel Alloy 718 AM Bench Artifacts 快速成型镍合金 718 的相组成和相变

IF 3.3 3区材料科学

Integrating Materials and Manufacturing Innovation Pub Date : 2024-02-05 DOI: 10.1007/s40192-023-00338-y

Fan Zhang, Aaron C. Johnston-Peck, Lyle E. Levine, Michael B. Katz, Kil-Won Moon, Maureen E. Williams, Sandra W. Young, Andrew J. Allen, Olaf Borkiewicz, Jan Ilavsky

{"title":"Phase Composition and Phase Transformation of Additively Manufactured Nickel Alloy 718 AM Bench Artifacts","authors":"Fan Zhang, Aaron C. Johnston-Peck, Lyle E. Levine, Michael B. Katz, Kil-Won Moon, Maureen E. Williams, Sandra W. Young, Andrew J. Allen, Olaf Borkiewicz, Jan Ilavsky","doi":"10.1007/s40192-023-00338-y","DOIUrl":"https://doi.org/10.1007/s40192-023-00338-y","url":null,"abstract":"Additive manufacturing (AM) technologies offer unprecedented design flexibility but are limited by a lack of understanding of the material microstructure formed under their extreme and transient processing conditions and its subsequent transformation during post-build processing. As part of the 2022 AM Bench Challenge, sponsored by the National Institute of Standards and Technology, this study focuses on the phase composition and phase evolution of AM nickel alloy 718, a nickel-based superalloy, to provide benchmark data essential for the validation of computational models for microstructural predictions. We employed high-energy synchrotron X-ray diffraction, in situ synchrotron X-ray scattering, as well as high-resolution transmission electron microscopy for our analyses. The study uncovers critical aspects of the microstructure in its as-built state, its transformation during homogenization, and its phase evolution during subsequent aging heat treatment. Specifically, we identified secondary phases, monitored the dissolution and coarsening of microstructural elements, and observed the formation and stability of γ’ and γ” phases. The results provide the rigorous benchmark data required to understand the atomic and microstructural transformations of AM nickel alloy 718, thereby enhancing the reliability and applicability of AM models for predicting phase evolution and mechanical properties.","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139768019","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

Ability to Simulate Absorption and Melt Pool Dynamics for Laser Melting of Bare Aluminum Plate: Results and Insights from the 2022 Asynchronous AM-Bench Challenge 模拟裸铝板激光熔化的吸收和熔池动力学的能力：2022 年异步 AM-Bench 挑战赛的结果与启示

IF 3.3 3区材料科学

Integrating Materials and Manufacturing Innovation Pub Date : 2024-02-01 DOI: 10.1007/s40192-023-00336-0

Brian J. Simonds, Jack Tanner, Alexandra Artusio-Glimpse, Niranjan Parab, Cang Zhao, Tao Sun, Paul A. Williams

{"title":"Ability to Simulate Absorption and Melt Pool Dynamics for Laser Melting of Bare Aluminum Plate: Results and Insights from the 2022 Asynchronous AM-Bench Challenge","authors":"Brian J. Simonds, Jack Tanner, Alexandra Artusio-Glimpse, Niranjan Parab, Cang Zhao, Tao Sun, Paul A. Williams","doi":"10.1007/s40192-023-00336-0","DOIUrl":"https://doi.org/10.1007/s40192-023-00336-0","url":null,"abstract":"The 2022 Asynchronous AM-Bench challenge was designed to test the ability of simulations to accurately predict laser power absorption as well as various melt pool behaviors (width, depth, and solidification) during laser melting of solid metal during stationary and scanned laser illumination. In this challenge, participants were asked to predict a series of experimental outcomes. Experimental data were obtained from a series of experiments performed at the Advanced Photon Source at Argonne National Laboratories in 2019. These experiments combined integrating sphere radiometry with high-speed X-ray imaging, allowing for the simultaneous recording of absolute laser power absorption and two-dimensional, projected images of the melt pool. All challenge problems were based on experiments using bare aluminum solid metal. Participants were provided with pertinent experimental information like laser power, scan speed, laser spot size, and material composition. Additionally, participants were given absorptance and X-ray imaging data from stationary and scanned laser experiments on solid Ti–6Al–4V that could be used for testing their models before attempting challenge problems. In total, this challenge received 56 submissions from eight different research groups for eight individual challenge problems. The data for this challenge, and associated information, are available for download from the NIST Public Data Repository. This paper summarizes the results from the 2022 Asynchronous AM-Bench challenge as well as discusses the lessons learned to help inform future challenges.","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139667154","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

Heat Source Model Development for Thermal Analysis of Laser Powder Bed Fusion Using Bayesian Optimization and Machine Learning 利用贝叶斯优化和机器学习为激光粉末床融合热分析开发热源模型

IF 3.3 3区材料科学

Integrating Materials and Manufacturing Innovation Pub Date : 2024-01-19 DOI: 10.1007/s40192-023-00334-2

Masahiro Kusano, Makoto Watanabe

{"title":"Heat Source Model Development for Thermal Analysis of Laser Powder Bed Fusion Using Bayesian Optimization and Machine Learning","authors":"Masahiro Kusano, Makoto Watanabe","doi":"10.1007/s40192-023-00334-2","DOIUrl":"https://doi.org/10.1007/s40192-023-00334-2","url":null,"abstract":"To understand the correlation between process, structures, and properties in laser powder bed fusion (L-PBF), it is essential to use numerical analysis as well as experimental approaches. A finite element thermal analysis uses a moving heat source model represented as a volumetric heat flux to simulate heat input by laser. Because of its computational efficiency, finite element thermal analysis is suitable for iterative procedures such as parametric study and process optimization. However, to obtain valid simulated results, the heat source model must be calibrated by comparison with experimental results for each laser scanning condition. The need for re-calibration limits the applicable window of laser scanning conditions in the thermal analysis. Thus, the current study developed a novel heat source model that is valid and precise under any laser scanning condition within a wide process window. As a secondary objective in the development, we quantitatively evaluated and compared the four heat source models proposed to date. It was found that the most suitable heat source model for the L-PBF is conical one among them. Then, a multiple linear regression analysis was performed to represent the heat source model as a function of laser power and scanning velocity. Consequently, the thermal analysis with the novel model is valid and precise within the wide process window of L-PBF.","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139509549","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

Enhancing Reproducibility in Precipitate Analysis: A FAIR Approach with Automated Dark-Field Transmission Electron Microscope Image Processing 提高沉淀分析的可重复性：利用自动暗场透射电子显微镜图像处理的 FAIR 方法

IF 3.3 3区材料科学

Integrating Materials and Manufacturing Innovation Pub Date : 2024-01-18 DOI: 10.1007/s40192-023-00331-5

{"title":"Enhancing Reproducibility in Precipitate Analysis: A FAIR Approach with Automated Dark-Field Transmission Electron Microscope Image Processing","authors":"","doi":"10.1007/s40192-023-00331-5","DOIUrl":"https://doi.org/10.1007/s40192-023-00331-5","url":null,"abstract":"<h3>Abstract</h3> High-strength aluminum alloys used in aerospace and automotive applications obtain their strength through precipitation hardening. Achieving the desired mechanical properties requires precise control over the nanometer-sized precipitates. However, the microstructure of these alloys changes over time due to aging, leading to a deterioration in strength. Typically, the size, number, and distribution of precipitates for a quantitative assessment of microstructural changes are determined by manual analysis, which is subjective and time-consuming. In our work, we introduce a progressive and automatable approach that enables a more efficient, objective, and reproducible analysis of precipitates. The method involves several sequential steps using an image repository containing dark-field transmission electron microscopy (DF-TEM) images depicting various aging states of an aluminum alloy. During the process, precipitation contours are generated and quantitatively evaluated, and the results are comprehensibly transferred into semantic data structures. The use and deployment of Jupyter Notebooks, along with the beneficial implementation of Semantic Web technologies, significantly enhances the reproducibility and comparability of the findings. This work serves as an exemplar of FAIR image and research data management.","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139495071","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