Готові списки джерел за темами / Microstructure

Добірка наукової літератури з теми "Microstructure"

Автор: Grafiati
Опубліковано: 4 червня 2021
Оновлено: 31 липня 2024

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Статті в журналах з теми "Microstructure"

1

Suzuki, Asuka, Yusuke Sasa, Makoto Kobashi, Masaki Kato, Masahito Segawa, Yusuke Shimono, and Sukeharu Nomoto. "Persistent Homology Analysis of the Microstructure of Laser-Powder-Bed-Fused Al–12Si Alloy." Materials 16, no. 22 (November 18, 2023): 7228. http://dx.doi.org/10.3390/ma16227228.

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The laser powder bed fusion (L-PBF) process provides the cellular microstructure (primary α phase surrounded by a eutectic Si network) inside hypo-eutectic Al–Si alloys. The microstructure changes to the particle-dispersed microstructure with heat treatments at around 500 °C. The microstructural change leads to a significant reduction in the tensile strength. However, the microstructural descriptors representing the cellular and particle-dispersed microstructures have not been established, resulting in difficulty in terms of discussion regarding the structure–property relationship. In this study, an attempt was made to analyze the microstructure in L-PBF-built and subsequently heat-treated Al–12Si (mass%) alloys using the persistent homology, which can analyze the spatial distributions and connections of secondary phases. The zero-dimensional persistent homology revealed that the spacing between adjacent Si particles was independent of Si particle size in the as-built alloy, whereas fewer Si particles existed near large Si particles in the heat-treated alloy. Furthermore, the first principal component of a one-dimensional persistent homology diagram would represent the microstructural characteristics from cellular to particle-dispersed morphology. These microstructural descriptors were strongly correlated with the tensile and yield strengths. This study provides a new insight into the microstructural indices describing unique microstructures in L-PBF-built alloys.
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2

Zeng, Qiu Lian, Zhong Guang Wang, and J. K. Shang. "Microstructural Effects on Low Cycle Fatigue of Sn-3.8Ag-0.7Cu Pb-Free Solder." Key Engineering Materials 345-346 (August 2007): 239–42. http://dx.doi.org/10.4028/www.scientific.net/kem.345-346.239.

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Low cycle fatigue behavior of Sn-3.8Ag-0.7Cu solder was investigated under fully reversed cyclic loading, with particular emphasis on microstructural effects. The LCF behavior of the solder with equiaxed microstructure was found to differ greatly from that of the solder with a dendrite microstructure. At a given total strain amplitude, the dendrite microstructure exhibited a much longer fatigue life than the equiaxed microstructure. Such a strong microstructural effect on fatigue life arose from the difference in cyclic deformation and fracture mechanisms between the two microstructures. A large number of microcracks along grain boundaries of the equiaxed structure solder developed with increasing cycling, while for the dendrite structure solder, cyclic deformation took place along the direction of the maximal shear stress during fatigue tests and microcracks initiated and propagated along shear deformation bands. Besides, the fatigue behavior of the dendritic microstructure was very sensitive to cyclic frequency whereas the fatigue behavior of the equiaxed microstructure showed less sensitivity to cyclic frequency.
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3

Trzciński, Jerzy, and Emilia Wójcik. "Application of microstructure classification for the assessment of the variability of geological-engineering and pore space properties in clay soils." Open Geosciences 11, no. 1 (June 12, 2019): 236–48. http://dx.doi.org/10.1515/geo-2019-0019.

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Abstract The performed investigations were focused on analysing the variability of geological-engineering properties in clay soils from central-eastern Europe with regard to microstructures. Thirty-nine soils differing in origin, lithology and microstructure type were selected for analysis. Results of studies on lithological and physical and mechanical parameters were analysed, coupled with quantitative microstructural parameters based on microstructure images. The relationships between lithological, physical and mechanical and pore space parameters were determined with regard to microstructure types. Sediment origin and diagenesis, and soil microstructure and pore space parameters had influence on the geological-engineering properties. Such approach allows for predicting engineering parameters based on soil microstructure types and their pore space parameters.
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4

Dolzhenko, Anastasiia, Marina Tikhonova, Rustam Kaibyshev, and Andrey Belyakov. "Microstructures and Mechanical Properties of Steels and Alloys Subjected to Large-Strain Cold-to-Warm Deformation." Metals 12, no. 3 (March 8, 2022): 454. http://dx.doi.org/10.3390/met12030454.

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The effect of large-strain cold-to-warm deformation on the microstructures and mechanical properties of various steels and alloys is critically reviewed. The review is mainly focused on the microstructure evolution, whereas the deformation textures are cursorily considered without detailed examination. The deformation microstructures are considered in a wide strain range, from early straining to severe deformations. Such an approach offers a clearer view of how the deformation mechanisms affect the structural changes leading to the final microstructures evolved in large strains. The general regularities of microstructure evolution are shown for different deformation methods, including conventional rolling/swaging and special techniques, such as equal channel angular pressing or torsion under high pressure. The microstructural changes during deformations under different processing conditions are considered as functions of total strain. Then, some important mutual relationships between the microstructural parameters, e.g., grain size vs. dislocation density, are revealed and discussed. Particular attention is paid to the mechanisms of microstructure evolution that are responsible for the grain refinement. The development of an ultrafine-grained microstructure during large strain deformation is considered in terms of continuous dynamic recrystallization. The regularities of the latter are discussed in comparison with conventional (discontinuous) dynamic recrystallization and grain subdivision (fragmentation) phenomenon. The structure–property relations are quantitatively represented for the structural strengthening, taking into account various mechanisms of dislocation retardation.
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5

Tian, Yan, Mingchun Zhao, Wenjian Liu, Jimou Zhang, Min Zhang, Hongying Li, Dengfeng Yin, and Andrej Atrens. "Comparison on Tensile Characteristics of Plain C–Mn Steel with Ultrafine Grained Ferrite/Cementite Microstructure and Coarse Grained Ferrite/Pearlite Microstructure." Materials 14, no. 9 (April 29, 2021): 2309. http://dx.doi.org/10.3390/ma14092309.

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This work investigated the tensile characteristics of plain C–Mn steel with an ultrafine grained ferrite/cementite (UGF/C) microstructure and coarse-grained ferrite/pearlite (CGF/P) microstructure. The tensile tests were performed at temperatures between 77 K and 323 K. The lower yield and the ultimate tensile strengths were significantly increased when the microstructure was changed from the CGF/P to the UGF/C microstructures, but the total elongation and the uniform elongation decreased. A microstructural change from the CGF/P microstructure to the UGF/C microstructure had an influence on the athermal component of the lower yield and the ultimate tensile strengths but not on the thermal component. The UGF/C microstructure with a higher carbon content provided a higher strength without losing ductility because cementite particles restrained necking.
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6

Hua, Tian, Ziyin Xiang, Xiangling Xia, Zhangling Li, Dandan Sun, Yuanzhao Wu, Yiwei Liu, Jie Shang, Jun Chen, and Runwei Li. "A Sensitivity-Optimized Flexible Capacitive Pressure Sensor with Cylindrical Ladder Microstructural Dielectric Layers." Sensors 23, no. 9 (April 27, 2023): 4323. http://dx.doi.org/10.3390/s23094323.

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Flexible capacitive pressure sensors have attracted extensive attention due to their dynamic response and good sensing capability for static and small pressures. Using microstructural dielectric layers is an effective method for improving performance. However, the current state of microstructure design is primarily focused on basic shapes and is largely limited by simulation results; there is still a great deal of potential for further innovation and improvement. This paper innovatively proposes to increase the ladder structure based on the basic microstructures, for example, the long micro-ridge ladder, the cuboid ladder, and cylindrical ladder microstructures. By comparing 9 kinds of microstructures including ladder structure through finite element simulation, it is found that the sensor with a cylindrical ladder microstructure dielectric layer has the highest sensitivity. The dielectric layers with various microstructures are obtained by 3D printed molds, and the sensor with cylindrical ladder microstructure dielectric layer has the sensitivity of 0.12 kPa−1, which is about 3.9 times higher than that without microstructure. The flexible pressure sensor developed by us boasts sensitivity-optimized and operational stability, making it an ideal solution for monitoring rainfall frequency in real time.
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7

Morri, A., L. Ceschini, M. Pellizzari, C. Menapace, F. Vettore, and E. Veneri. "Effect of the Austempering Process on the Microstructure and Mechanical Properties of 27MnCrB5-2 Steel." Archives of Metallurgy and Materials 62, no. 2 (June 1, 2017): 643–51. http://dx.doi.org/10.1515/amm-2017-0094.

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AbstractThe effect of austempering parameters on the microstructure and mechanical properties of 27MnCrB5-2 steel has been investigated by means of: dilatometric, microstructural and fractographic analyses; tensile and Charpy V-notch (CVN) impact tests at room temperature and a low temperature.Microstructural analyses showed that upper bainite developed at a higher austempering temperature, while a mixed bainitic-martensitic microstructure formed at lower temperatures, with a different amount of bainite and martensite and a different size of bainite sheaf depending on the temperature. Tensile tests highlighted superior yield and tensile strengths (≈30%) for the mixed microstructure, with respect to both fully bainitic and Q&T microstructures, with only a low reduction in elongation to failure (≈10%). Impact tests confirmed that mixed microstructures have higher impact properties, at both room temperature and a low temperature.
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8

Müller, Martin, Marie Stiefel, Björn-Ivo Bachmann, Dominik Britz, and Frank Mücklich. "Overview: Machine Learning for Segmentation and Classification of Complex Steel Microstructures." Metals 14, no. 5 (May 7, 2024): 553. http://dx.doi.org/10.3390/met14050553.

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The foundation of materials science and engineering is the establishment of process–microstructure–property links, which in turn form the basis for materials and process development and optimization. At the heart of this is the characterization and quantification of the material’s microstructure. To date, microstructure quantification has traditionally involved a human deciding what to measure and included labor-intensive manual evaluation. Recent advancements in artificial intelligence (AI) and machine learning (ML) offer exciting new approaches to microstructural quantification, especially classification and semantic segmentation. This promises many benefits, most notably objective, reproducible, and automated analysis, but also quantification of complex microstructures that has not been possible with prior approaches. This review provides an overview of ML applications for microstructure analysis, using complex steel microstructures as examples. Special emphasis is placed on the quantity, quality, and variance of training data, as well as where the ground truth needed for ML comes from, which is usually not sufficiently discussed in the literature. In this context, correlative microscopy plays a key role, as it enables a comprehensive and scale-bridging characterization of complex microstructures, which is necessary to provide an objective and well-founded ground truth and ultimately to implement ML-based approaches.
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9

Abdalla, Ayad Omran, Astuty Amrin, Roslina Mohammad, and M. A. Azmah Hanim. "Microstructural Study of Newly Designed Ti-6Al-1Fe Alloy through Deformation." Solid State Phenomena 264 (September 2017): 54–57. http://dx.doi.org/10.4028/www.scientific.net/ssp.264.54.

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Recently, iron (Fe) is introduced to substitute vanadium (V) in Ti-alloy. Therefore, new (α+β) titanium alloy, Ti-6Al-1Fe was designed through a complete replacement of V by Fe with major composition modifications of Ti-6Al-4V. This new alloy is believed could provide similar properties of Ti-6Al-4V through modification of its microstructures. Different heat treatments can lead to a diversity of microstructural permutations and combinations. Thus, it is very crucial to study in-depth understanding about the microstructure of Ti-6Al-1Fe. Results reveal that the microstructure of as-received alloy is a typical fine lamellar microstructure. The bi-modal microstructure can be obtained by hot rolling below beta-transus temperature (Tβ) followed by recrystallization treatment at 925°C. While cold rolling followed by recrystallization treatment at 925°C produce equiaxed microstructure.
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10

Gallardo-Basile, Francisco-José, Yannick Naunheim, Franz Roters, and Martin Diehl. "Lath Martensite Microstructure Modeling: A High-Resolution Crystal Plasticity Simulation Study." Materials 14, no. 3 (February 2, 2021): 691. http://dx.doi.org/10.3390/ma14030691.

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Lath martensite is a complex hierarchical compound structure that forms during rapid cooling of carbon steels from the austenitic phase. At the smallest, i.e., ‘single crystal’ scale, individual, elongated domains, form the elemental microstructural building blocks: the name-giving laths. Several laths of nearly identical crystallographic orientation are grouped together to blocks, in which–depending on the exact material characteristics–clearly distinguishable subblocks might be observed. Several blocks with the same habit plane together form a packet of which typically three to four together finally make up the former parent austenitic grain. Here, a fully parametrized approach is presented which converts an austenitic polycrystal representation into martensitic microstructures incorporating all these details. Two-dimensional (2D) and three-dimensional (3D) Representative Volume Elements (RVEs) are generated based on prior austenite microstructure reconstructed from a 2D experimental martensitic microstructure. The RVEs are used for high-resolution crystal plasticity simulations with a fast spectral method-based solver and a phenomenological constitutive description. The comparison of the results obtained from the 2D experimental microstructure and the 2D RVEs reveals a high quantitative agreement. The stress and strain distributions and their characteristics change significantly if 3D microstructures are used. Further simulations are conducted to systematically investigate the influence of microstructural parameters, such as lath aspect ratio, lath volume, subblock thickness, orientation scatter, and prior austenitic grain shape on the global and local mechanical behavior. These microstructural features happen to change the local mechanical behavior, whereas the average stress–strain response is not significantly altered. Correlations between the microstructure and the plastic behavior are established.
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Дисертації з теми "Microstructure"

1

Jensen, Jens A. D. "Engineering of metal microstructures : process-microstructure-property relationships for electrodeposits /." Linköping : Univ, 2002. http://www.bibl.liu.se/liupubl/disp/disp2002/tek784s.pdf.

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2

Smith, Benjamin Daniel. "Microstructure-sensitive plasticity and fatigue of three titanium alloy microstructures." Thesis, Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49237.

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Titanium alloys are employed in many advanced engineering applications due to their exceptional properties, i.e., a high strength-to-weight ratio, corrosion resistance, and high temperature strength. The performance of titanium alloys is known to be strongly affected by its inherent microstructure, which forms as a result of its thermo-mechanical processing. These microstructures produce compromise relationships between beneficial and detrimental effects on the alloy's performance. To study these structure-property relationships, two distinct crystal plasticity algorithms have been calibrated to data acquired from cyclic deformation experiments performed on three different Ti microstructures: (1) Ti-6Al-4V beta-annealed , (2) Ti-18 solution-treated, age-hardened (STA), and (3) Ti-18 beta-annealed, slow-cooled, age-hardened (BASCA). The calibrated models have been utilized to simulate fatigue loading of variant microstructures to investigate the influence of mean grain size, crystallographic texture, and phase volume fraction. The driving force for fatigue crack nucleation and propagation is quantified through the calculation of relevant fatigue indicator parameters (FIPs) and radial correlation functions are employed to study the correlation between favorably oriented slip systems and the extreme value FIP locations. The computed results are utilized to observe fatigue performance trends associated with changes to key microstructural attributes.
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3

DeCost, Brian L. "Microstructure Representations: Applied Computer Vision Methods for Microstructure Characterization." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/764.

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Recent advances in computing power and automated microstructural image acquisition have opened the doors to data-driven quantitative microstructure analysis. Extraction of salient microstructure features is a crucial enabling component in this rapidly developing field of research; in the past decade the computer vision community has made enormous progress in this area, much of which has gone relatively unexplored by the quantitative microstructure analysis community. This dissertation explores applications of image texture recognition algorithms to engineer efficiently computable generic microstructure descriptors, enabling quantitative microstructure comparisons between and across a wide variety of materials systems. The literature review serves as a broad, high-level introduction for the materials scientist to some of the major themes in image recognition, along with some brief discussion of their relationship to contemporary microstructure science. After establishing that these image texture recognition algorithms can be effectively applied to classify diverse microstructure datasets, I begin to explore novel materials science applications. These include characterization and qualification of powder materials, exploratory analysis of large microstructure datasets, and extraction of quantitative relationships between materials processing and properties metadata and microstructural image features. The fusion of microstructure image analysis and contemporary machine vision techniques will facilitate development of robust autonomous microscopy systems, and may support quantitative engineering standards for complex hierarchical microstructure systems.
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4

Visarraga, Darrin Bernardo. "Heat transport models with distributed microstructure." Access restricted to users with UT Austin EID, 2001. http://wwwlib.umi.com/cr/utexas/fullcit?p3036605.

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5

Tse, Jonathan. "Market microstructure modelling." Thesis, University of Oxford, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540272.

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6

Borhani, Ehsan. "Microstructure and Mechanical Property of Heavily Deformed Al-Sc Alloy Having Different Starting Microstructures." 京都大学 (Kyoto University), 2012. http://hdl.handle.net/2433/152522.

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7

Lee, Eunha. "Microstructure evolution and microstructure/mechanical properties relationships in [alpha]+[beta] titanium alloys." Connect to this title online, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1092756139.

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Thesis (Ph. D.)--Ohio State University, 2004.
Title from first page of PDF file. Document formatted into pages; contains xxiii, 229 p.; also includes graphics (some col.) Includes bibliographical references (p. 224-229). Available online via OhioLINK's ETD Center
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8

Kong, Lingrong. "Comportement microstructural de l'argile de Shanghai et modèle élastoplastique avec prise en compte de la microstructure." Nantes, 2007. http://www.theses.fr/2007NANT2047.

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Les modèles de comportement pour l’argile basés sur une approche macromécanique peuvent se révéler insuffisants pour décrire les courbes contraintes- déformations. Nous développons ici un modèle prenant en compte l’évolution de la microstructure et basé sur des expérimentations sur l’argile molle de Shanghai. Ce travail est découpé en quatre parties. Dans la première partie, on identifie la microstructure de l’argile de Shanghai d’un point de vue minéralogique, par microscopie électronique à balayage et analyse X. Ces analyses permettent de quantifier la forme, la taille moyenne et l’orientation des particules sur des plans de coupe horizontaux et verticaux. Ces analyses sont menées pour différents niveaux de contrainte au cours d’essais oedométriques sur l’argile naturelle puis l’argile remaniée. On peut conclure que le sol présente une évolution graduelle de la taille des particules et de leur orientation, créant une anisotropie matérielle pendant le chargement. Dans la deuxième partie, la relation entre la distribution des tailles de pores et la perméabilité, pour différents niveaux de contrainte, est établie par de 0,2 µm à 1,2 µm. Cette taille caractéristique diminue avec l’accroissement de la contrainte verticale appliquée. Parmi les différentes théories permettant de corréler la perméabilité et la distribution des tailles de pore, le modèle du rayon hydraulique a été adopté. L’équation quantitative a ensuite été dérivée. Dans la troisième partie, on étend le modèle élastoplastique considérant la microstructure des milieux granulaires (Chang & Hicher, 2005) aux matériaux fins argileux. On fait l’hypothèse que la microstructure de l'argile est faite d'agrégats qui ne peuvent pas être détruits pendant le chargement. La déformation est calculée en intégrant le déplacement aux contacts des particules dans toutes les orientations. Le comportement de contact des particules suit une loi élastoplastique basée sur le critère de Mohr-Coulomb. La loi élastique de Hertz-Mindlin est employée pour la partie élastique. Une double surface de charge est utilisée pour refléter le comportement plastique. Les prédictions du modèle sont comparées aux résultats expérimentaux pour les essais triaxiaux drainés et non-drainés sur l’argile normalement consolidée et surconsolidée. La comparaison montre que le modèle proposé reproduit les caractéristiques principales du comportement de l’argile. La quatrième partie concerne un modèle élastoplastique microstructural considérant l’anisotropie de l’argile. Un tenseur symétrique du second ordre est introduit pour rendre compte de l’évolution de la rigidité des plans de contact, des paramètres d’écrouissage, et du coefficient de contractance – dilatance en fonction de l’orientation des contacts, afin de décrire le comportement anisotrope du matériau. Des essais triaxiaux drainés et non-drainés avec chargement vertical et horizontal ont été effectués sur l'argile de Shanghai. Ces essais ont été simulés par le modèle proposé ainsi que des essais sur l’argile de San Francisco, et des essais triaxiaux non-drainés sur une kaolinite sous différentes conditions. La comparaison entre les simulations et les mesures montre que le modèle peut décrire le comportement de l’anisotropie inhérente et celle induite par un état de contrainte anisotrope sur l'argile
The constitutive models of soft soils based on traditional macro-mechanics are sometimes not suitable to describe the stress – strain relations. Therefore, we developed a new constitutive model considering the microstructure of a marine soft clay on which physical and mechanical tests were carried out. In the first part, scanning electron microscope and X-scans aimed at identifying the microstructure of the clay and its evolution during one dimensional consolidation tests. Those methods also give some quantitative and useful information (particle shape, mean size and orientation) related to the applied vertical stress. We concluded that the soil exhibits a gradual damage and material anisotropy. These results were also compared to the results obtained on the same remolded clay. In the second part, mercury intrusion tests were carried out to investigate the relation between the pore size distribution and permeability. The main size distribution varied from 0. 2 µm to 1. 2 µm and decreased whereas the vertical stress increases. The hydraulic-radius model was then introduced to explain such an evolution. The third part concerns the description of the elasto-plastic model considering microstructure extended from the previous work of Chang and Hicher (2005) to the behavior of soft clays. The clay microstructure was assumed to be made of platelet aggregates which could not be destroyed during loading. The deformation was computed by integrating displacements at the particle contacts in all orientations. The behavior of contact planes was assumed to follow an elastic-plastic law based on a Mohr-Coulomb criterion. A Hertz-Mindlin’s elastic law was used for the elastic part and a double yield surface was used to reflect the plastic behavior. The predictions of the constitutive model were compared to experimental results for normally and over-consolidated clays under drained and undrained triaxial tests. They showed that the model was able to reproduce the main features of the clay behavior. Moreover, the model enables to describe the evolution of stresses between aggregates on the contact plane and therefore the failure of clays based on microstructural considerations. The fourth part was devoted to the analysis of anisotropic clays. A second-order symmetric fabric tensor was introduced to model the stiffness of contact planes, hardening, the dilation coefficient, to capture the inherent and stress-induced anisotropic behavior of clays. Drained and undrained triaxial tests with loading in vertical and horizontal directions were performed on Shanghai clay
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9

Wearing, Cameron. "Sodicity and soil microstructure /." [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18523.pdf.

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10

Hoffmann, Peter. "Essays in Market Microstructure." Doctoral thesis, Universitat Pompeu Fabra, 2011. http://hdl.handle.net/10803/38703.

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This thesis covers three topics in Market Microstructure. Chapter 1 demonstrates that market access frictions may play a significant role in the competition between trading platforms. Analyzing a recent dataset of the trading activity in French and German stocks, we provide evidence that the incumbent markets dominate because the sole market entrant exposes liquidity providers to an excessive adverse selection risk due to a lack of noise traders. Chapter 2 presents a theoretical model of price formation in a dynamic limit order market with slow human traders and fast algorithmic traders. We show that in most cases, algorithmic trading has a detrimental effect on human traders’ welfare. Finally, Chapter 3 empirically analyzes the role of pre-trade transparency in call auctions. Comparing the trading mechanisms in place on the French and German stock exchanges, we find that transparency is associated with higher trading volume, greater liquidity, and better price discovery.
Esta tesis estudia tres temas diferentes de la microestructura de los mercados financieros. El capítulo 1 demuestra que fricciones en el acceso al mercado pueden desempeñar un papel significativo en la competencia entre plataformas de negociación de activos. El análisis de un conjunto de datos recientes de la actividad en acciones francesas y alemanas demuestra que los mercados primarios dominan debido a que el único mercado satélite expone los proveedores de liquidez a un riesgo excesivo de selección adversa, causado por una falta de noise traders. El capítulo 2 presenta un modelo teórico de formación de precios en un mercado dinámico con limit order book poblado por agentes humanos lentos y agentes algorítmicos rápidos. Se demuestra que, en la mayoría de los casos, la negociación algorítmica tiene un efecto negativo sobre el bienestar de agentes humanos. Por último, el capítulo 3 analiza empíricamente el papel de la transparencia pre-negociación en las subastas de apertura y de cierre. Comparando los mecanismos en las bolsas francesas y alemanas, encontramos que la transparencia está asociada con un volumen mayor, una liquidez mayor y un mejor price discovery.
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Книги з теми "Microstructure"

1

Bennett, Richard H., and Matthew H. Hulbert. Clay Microstructure. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4684-2.

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2

Abergel, Frédéric, Jean-Philippe Bouchaud, Thierry Foucault, Charles-Albert Lehalle, and Mathieu Rosenbaum, eds. Market Microstructure. Oxford, UK: John Wiley & Sons Ltd, 2012. http://dx.doi.org/10.1002/9781118673553.

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3

H, Hulbert Matthew, ed. Clay microstructure. Boston: International Human Resources Development Corp., 1986.

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4

Capriz, Gianfranco. Continua with Microstructure. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-3584-2.

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5

Enamel Microstructure Workshop, University of Bonn (1994 Andernach, Rhine). Tooth enamel microstructure. Rotterdam: A.A. Balkema, 1997.

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6

Capriz, G. Continua with Microstructure. New York, NY: Springer New York, 1989.

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7

United States. National Aeronautics and Space Administration., ed. Microstructure: Property correlation. [Washington, D.C: National Aeronautics and Space Administration, 1990.

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8

Peggs, ID, ed. Geosynthetics: Microstructure and Performance. 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959: ASTM International, 1990. http://dx.doi.org/10.1520/stp1076-eb.

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9

Kinderlehrer, David, Richard James, Mitchell Luskin, and Jerry L. Ericksen, eds. Microstructure and Phase Transition. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4613-8360-4.

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Axelrad, David R., and Wolfgang Muschik, eds. Constitutive Laws and Microstructure. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83303-8.

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Частини книг з теми "Microstructure"

1

Fang, Fengzhou, and Nan Zhang. "Microstructure." In CIRP Encyclopedia of Production Engineering, 1–5. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-642-35950-7_16727-1.

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Rindler, Filip. "Microstructure." In Universitext, 227–68. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77637-8_9.

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3

Miller, Michael K. "Microstructure." In Bulk Metallic Glasses, 117–45. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-48921-6_5.

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4

Fang, Fengzhou, and Nan Zhang. "Microstructure." In CIRP Encyclopedia of Production Engineering, 1197–202. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-53120-4_16727.

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5

Gooch, Jan W. "Microstructure." In Encyclopedic Dictionary of Polymers, 462. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_7489.

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6

Gottstein, Günter. "Microstructure." In Physical Foundations of Materials Science, 5–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-09291-0_2.

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7

Hernando, Isabel, Empar Llorca, and Amparo Quiles. "Microstructure." In Handbook of Dairy Foods Analysis, 323–38. 2nd ed. Second edition. | Boca Raton : CRC Press, 2021.: CRC Press, 2021. http://dx.doi.org/10.1201/9780429342967-16.

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8

Rebohle, Lars, and Wolfgang Skorupa. "Microstructure." In Rare-Earth Implanted MOS Devices for Silicon Photonics, 5–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14447-9_2.

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9

Hernando, Isabel, Empar Llorca, and Amparo Quiles. "Microstructure." In Handbook of Seafood and Seafood Products Analysis, 169–89. 2nd ed. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003289401-11.

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10

Bennett, Richard H., and Matthew H. Hulbert. "Introduction." In Clay Microstructure, 1–3. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4684-2_1.

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Тези доповідей конференцій з теми "Microstructure"

1

Xu, Hongyi, Ruoqian Liu, Alok Choudhary, and Wei Chen. "A Machine Learning-Based Design Representation Method for Designing Heterogeneous Microstructures." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34570.

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In designing microstructural materials systems, one of the key research questions is how to represent the microstructural design space quantitatively using a descriptor set that is sufficient yet small enough to be tractable. Existing approaches describe complex microstructures either using a small set of descriptors that lack sufficient level of details, or using generic high order microstructure functions of infinite dimensionality without explicit physical meanings. We propose a new machine learning-based method for identifying the key microstructure descriptors from vast candidates as potential microstructural design variables. With a large number of candidate microstructure descriptors collected from literature covering a wide range of microstructural material systems, a 4-step machine learning-based method is developed to eliminate redundant microstructure descriptors via image analyses, to identify key microstructure descriptors based on structure-property data, and to determine the microstructure design variables. The training criteria of the supervised learning process include both microstructure correlation functions and material properties. The proposed methodology effectively reduces the infinite dimension of the microstructure design space to a small set of descriptors without a significant information loss. The benefits are demonstrated by an example of polymer nanocomposites optimization. We compare designs using key microstructure descriptors versus using empirically-chosen microstructure descriptors to validate the proposed method.
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2

Jonsson, Katherine, Douglas G. Ivey, Hani Henein, Shahrooz Nafisi, Laurie Collins, Thomas Garcin, and Warren Poole. "The Effect of Microstructure on Tensile Behaviour of X80 Microalloyed Steel." In 2012 9th International Pipeline Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/ipc2012-90685.

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A high degree of work hardening is desirable for steels to be employed in strain-based pipeline designs. In an effort to enhance work hardening characteristics, this study was conducted to determine the effect of thermal treatment on microstructural development and the subsequent relationship between microstructure and tensile behaviour of high strength microalloyed line pipe steel. A series of thermal schedules was applied to X80 steel samples using a Gleeble thermo-mechanical simulator in order to generate a variety of microstructures. The microstructures were quantified by calculating the phase fraction of individual phases using scanning electron microscopy (SEM). A focused ion beam (FIB) instrument was used to prepare electron transparent samples of specific grains that were characterized using transmission electron microscopy (TEM). The X80 microstructures were composed mostly of bainitic and ferritic grains with isolated pockets of martensite and M-A islands due to local carbon segregation. The effect of thermal treatment on microstructural evolution was determined based on varying the interrupt temperature, re-heat temperature and hold time at elevated temperatures. The overall effect of microstructure on the mechanical properties was evaluated, with a particular focus on hardness values and the shape of the stress-strain curves. The effect of thermal history and microstructure development on the work hardening characteristics was also determined.
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3

Rangarajan, Aswath, and Veera Sundararaghavan. "Design of Microstructure Response Using a Complex Step Plasticity Approach." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-39011.

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Microstructure-sensitive design is performed using a newly developed Multiscale Complex Step Method. Algorithmic implementation of the complex-step method is based on conventional multiscale Taylor or FE2 direct model, with the difference being that the microstructural deformation gradient is modeled as a complex number with a small imaginary component. We introduce methods to calculate derivatives of microstructural fields with respect to loading parameters. The sensitivities are used to identify optimal microstructures with desired elastic and plastic properties.
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4

Xu, Leidong, Kiarash Naghavi Khanghah, and Hongyi Xu. "Design of Mixed-Category Stochastic Microstructures: A Comparison of Curvature Functional-Based and Deep Generative Model-Based Methods." In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-114601.

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Abstract Bridging the gaps among various categories of stochastic microstructures remains a challenge in the design representation of microstructural materials. Each microstructure category requires certain unique mathematical and statistical methods to define the design space (design representation). The design representation methods are usually incompatible between two different categories of stochastic microstructures. The common practice of pre-selecting the microstructure category and the associated design representation method before conducting rigorous computational design limits the design freedom and reduces the possibility of obtaining innovative microstructure designs. To overcome this issue, this paper proposes and compares two methods, the deep generative modeling-based method and the curvature functional-based method, to understand their pros and cons in designing mixed-category stochastic microstructures for desired properties. For the deep generative modeling-based method, the Variational Autoencoder is employed to generate an unstructured latent space as the design space. For the curvature functional-based method, the microstructure geometry is represented by curvature functionals, of which the functional parameters are employed as the microstructure design variables. Regressors of the microstructure design variables-property relationship are trained for microstructure design optimization. A comparative study is conducted to understand the relative merits of these two methods in terms of computational cost, continuous transition, design scalability, design diversity, dimensionality of the design space, interpretability of the statistical equivalency, and design performance.
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5

Li, Xiaolin, Zijiang Yang, L. Catherine Brinson, Alok Choudhary, Ankit Agrawal, and Wei Chen. "A Deep Adversarial Learning Methodology for Designing Microstructural Material Systems." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85633.

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In Computational Materials Design (CMD), it is well recognized that identifying key microstructure characteristics is crucial for determining material design variables. However, existing microstructure characterization and reconstruction (MCR) techniques have limitations to be applied for materials design. Some MCR approaches are not applicable for material microstructural design because no parameters are available to serve as design variables, while others introduce significant information loss in either microstructure representation and/or dimensionality reduction. In this work, we present a deep adversarial learning methodology that overcomes the limitations of existing MCR techniques. In the proposed methodology, generative adversarial networks (GAN) are trained to learn the mapping between latent variables and microstructures. Thereafter, the low-dimensional latent variables serve as design variables, and a Bayesian optimization framework is applied to obtain microstructures with desired material property. Due to the special design of the network architecture, the proposed methodology is able to identify the latent (design) variables with desired dimensionality, as well as capturing complex material microstructural characteristics. The validity of the proposed methodology is tested numerically on a synthetic microstructure dataset and its effectiveness for materials design is evaluated through a case study of optimizing optical performance for energy absorption. Additional features, such as scalability and transferability, are also demonstrated in this work. In essence, the proposed methodology provides an end-to-end solution for microstructural design, in which GAN reduces information loss and preserves more microstructural characteristics, and the GP-Hedge optimization improves the efficiency of design exploration.
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6

Xu, Hongyi, Yang Li, Catherine Brinson, and Wei Chen. "Descriptor-Based Methodology for Designing Heterogeneous Microstructural Materials System." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12232.

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In designing a microstructural materials system, there are several key questions associated with design representation, design evaluation, and design synthesis: how to quantitatively represent the design space of a heterogeneous microstructure system using a small set of design variables, how to efficiently reconstruct statistically equivalent microstructures for design evaluation, and how to quickly search for the optimal microstructure design to achieve the desired material properties. This paper proposes a new descriptor-based methodology for designing microstructural materials systems. A descriptor-based characterization method is proposed to provide a quantitative representation of material morphology using a small set of microstructure descriptors covering features of material composition, dispersion status, and phase geometry at different levels of representation. A descriptor-based multi-phase microstructure reconstruction algorithm is developed which allows efficient stochastic reconstruction of microstructures for Finite Element Analysis (FEA) of material behavior. The choice of descriptors for polymer nanocomposites is verified by establishing a mapping between the finite set of descriptors and the infinite dimensional correlation function. Finally, the descriptor-based representation allows the use of parametric optimization approach to search the optimal microstructure design that meets the target material properties. To improve the search efficiency, this paper employs state-of-the-art computational design methods such as Design of Experiment (DOE), metamodeling, statistical sensitivity analysis, and multi-objective optimization. The proposed methodology is demonstrated using the design of a polymer nanocomposites system.
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7

Isobe, Yoshihiro, Junji Etoh, Mitsuyuki Sagisaka, Takashi Matsunaga, Paula Freyer, Frank Garner, and Taira Okita. "Ultrasonic NDE for Irradiation-Induced Material Degradations." In 2013 21st International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/icone21-16850.

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We have developed a prediction model of ultrasonic wave changes caused by microstructural changes in 304 stainless steel based on the theory of ultrasonic wave propagation and experimental results. The correlation between microstructure and ultrasonic waves was simulated in the model with microstructure components such as grain boundaries, voids, precipitates and dislocations taken into account. Material parameters used for the prediction model were determined from the literature, microscopic observations, and experimental results obtained from archive materials. Calculations were performed assuming respective and integrated changes of irradiation-induced microstructures and spectrum changes of ultrasonic signals due to microstructural changes were evaluated. Calculations for archive material were also performed for validation, and the results were reasonably consistent with experimental data. It was found that by selecting appropriate indicators one can identify the nature of microstructures and quantify the microstructural changes due to irradiation.
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8

Zhao, Xinyu, Ashif Iquebal, Huifeng Sun, and Hao Yan. "Simultaneous Material Microstructure Classification and Discovery via Hidden Markov Modeling of Acoustic Emission Signals." In ASME 2020 15th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/msec2020-8454.

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Abstract Acoustic emission (AE) signals have been widely employed for tracking material properties and structural characteristics. In this study, we aim to analyze the AE signals gathered during a scanning probe lithography process to classify the known microstructure types and discover unknown surface microstructures/anomalies. To achieve this, we developed a Hidden Markov Model to consider the temporal dependency of the high-resolution AE data. Furthermore, we compute the posterior classification probability and the negative likelihood score for microstructure classification and discovery. Subsequently, we present a diagnostic procedure to identify the dominant AE frequencies that allow us to track the microstructural characteristics. Finally, we apply the proposed approach to identify the surface microstructures of additively manufactured Ti-6Al-4V and show that it not only achieved a high classification accuracy (e.g., more than 90%) but also correctly identified the microstructural anomalies that may be subjected further investigation to discover new material phases/properties.
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9

Wu, Yulun, and Yumeng Li. "How to Encode Microstructure in Machine Learning: A Comparison Study." In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-116704.

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Abstract Accurately predicting the response of materials under different loading conditions is crucial for designing and developing new materials with desired properties. However, this process can be computationally expensive and challenging, especially for heterogeneous materials with complex microstructures. Recently, machine learning has been widely used to address the challenge for developing predictive models for various material systems with reduced reliance on extensive experimental testings and repetitive expensive physics simulations. The microstructure of a material plays a critical role in determining its properties, making it a key factor that needs to be accounted for in predictive modeling. Heterogeneous materials, specifically, often have complex microstructures with numerous features like pores, inclusions, and grain boundaries, which need to be accurately captured but is hard to be quantified for developing machine learning based predictive models. Therefore, accurate encoding of microstructural features is essential for making reliable predictions. Nevertheless, how to effectively and efficiently capture the complex microstructural features in developing machine learning based predictive models largely remains an open question for researchers in the field of materials science. In this paper, we present a comparison study of different encoding methods for microstructures in machine learning models. Specifically, we investigate pre-defined encoding methods and automatic encoding methods for a synthetic heterogeneous material system. the performance of each machine learning model is evaluated by predicting material responses such as strain energy. Our results show that convolutional neural networks (CNNs) have the ability to auto-encode the microstructure information of material and make promising prediction, especially when good pre-defined descriptors are not available. Overall, this study provides valuable insights into the performance of different encoding methods for microstructures in machine learning models, and can inform the development of more accurate and efficient models for materials science applications.
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10

Kim, Kyu Tae, Sang Gi Ko, and Jong Man Han. "Effects of Microstructural Inhomogeneity on HIC Susceptibility and HIC Evaluation Methods for Linepipe Steels for Sour Service." In 2014 10th International Pipeline Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/ipc2014-33341.

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It has been well documented that slab internal quality is one of the key factors for reduced susceptibility of hydrogen induced cracking (HIC) in line pipe steels designed for sour gas service. In addition, the creation of a homogeneous microstructure which is heavily influenced by the slab internal quality is also a critical key parameter to reduce the HIC susceptibility in higher strength line pipe steel grade X60 and above. For the application of deep sea linepipe exposed to higher external pressure environments, heavy gauge in combination with higher strength steel is essential. Homogeneity of the steel microstructure is a key to success for thicker plates used in sour service HIC applications in combination with a deep sea environment. In this paper, various microstructures were compared along with an evaluation of the effects of the various microstructures on HIC susceptibility in grades X52, X65 and X70 designed for sour service. The various microstructures compared consisted of polygonal ferrite and pearlite in the X52 and polygonal ferrite, pearlite, acicular ferrite and bainite in the X65 and X70. The effect of microstructural inhomogeneity on HIC susceptibility was comparatively lower for the X52 than that of the X65 and X70. The microstructure of grade X65 and X70 were different due to the different conditions of rolling and cooling that were applied. Grades X65/X70 had a microstructure of polygonal ferrite/pearlite with bainite islands that resulted in a high crack length ratio (CLR) value caused by different hardness regions across the microstructural matrix. A homogeneous fine acicular ferrite microstructure produced by optimizing temperature control during rolling and cooling showed no hydrogen induced cracking. In addition, this alloy/process/microstructure design resulted in improved toughness results in low temperature drop weight tear test (DWTT). This paper will describe the successful production results of plate and pipe for high strength heavier gauge line pipe steels with highly homogeneous microstructures designed for sour service by controlling chemical design and process conditions in rolling and cooling. In addition, HIC evaluation methods utilizing both a traditional NACE TM0284 method versus that of a Scan-UT method were conducted and compared. A proposal to make the NACE TM0284 testing method more reliable by using Scan-UT method will be presented.
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Звіти організацій з теми "Microstructure"

1

Moore, A. S., C. A. Thomas, and T. M. Reese. Microstructure Filled Hohlraums. Office of Scientific and Technical Information (OSTI), February 2017. http://dx.doi.org/10.2172/1345335.

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2

Schroers, Jan, and Jittisa Ketkaew. Using Artificial Microstructures to Understand Microstructure Property Relationship-Toughening Mechanisms in Metallic Glass. Office of Scientific and Technical Information (OSTI), July 2023. http://dx.doi.org/10.2172/1989817.

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3

Gregg, Michael C., and Jack B. Miller. Modular Microstructure Profiler (MMP). Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada605602.

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4

Olson, Gregory B. Dynamic Microstructure Design Consortium. Fort Belvoir, VA: Defense Technical Information Center, March 2011. http://dx.doi.org/10.21236/ada544619.

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5

Asenath-Smith, Emily, Ross Lieblappen, Susan Taylor, Reed Winter, Terry Melendy, Robert Moser, and Robert Haehnel. Observation of crack arrest in ice by high aspect ratio particles during uniaxial compression. Engineer Research and Development Center (U.S.), February 2022. http://dx.doi.org/10.21079/11681/43145.

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In nature, ice frequently contains dissolved solutes or entrapped particles, which modify the microstructure and mechanical properties of ice. Seeking to understand the effect of particle shape and geometry on the mechanical properties of ice, we performed experiments on ice containing 15 wt% silica spheres or rods. Unique to this work was the use of 3-D microstructural imaging in a -10ºC cold room during compressive loading of the sample. The silica particles were present in the ice microstructure as randomly dispersed aggregates within grains and at grain boundaries. While cracks originated in particle-free regions in both sphere- and rod-containing samples, the propagation of cracks was quite different in each type of sample. Cracks propagated uninhibited through aggregates of spherical particles but were observed to arrest at and propagate around aggregates of rods. These results imply that spherical particles do not inhibit grain boundary sliding or increase viscous drag. On the other hand, silica rods were found to span grains, thereby pinning together the microstructure of ice during loading. These results provide insights into mechanisms that can be leveraged to strengthen ice.
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6

Twesten, R. D., J. M. Millunchick, S. R. Lee, D. M. Follstaedt, E. D. Jones, S. P. Ahrenkiel, Y. Zhang, and A. Mascarenhas. Microstructure of compositionally modulated InAlAs. Office of Scientific and Technical Information (OSTI), December 1996. http://dx.doi.org/10.2172/453541.

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7

Caturla, M. Microstructure evolution in irradiated materials. Office of Scientific and Technical Information (OSTI), November 1999. http://dx.doi.org/10.2172/15002353.

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8

Lawn, Brian R., Peter L. Swanson, Carolyn J. Fairbanks, Bernard J. Hockey, and Yiu-Wing Mai. Strength and Microstructure of Ceramics. Fort Belvoir, VA: Defense Technical Information Center, November 1987. http://dx.doi.org/10.21236/ada190712.

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9

Gibson, Carl H. Analysis of Soviet Microstructure Data. Fort Belvoir, VA: Defense Technical Information Center, April 1997. http://dx.doi.org/10.21236/ada325698.

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10

Chatterjee, Tapan, Stacey Kerwien, and Elias Jelis. Microstructure Analysis of Boron Nitride. Fort Belvoir, VA: Defense Technical Information Center, September 2009. http://dx.doi.org/10.21236/ada505516.

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