arXiv subjects

Sang-Hyeok Lee

Publications and source records attributed to Sang-Hyeok Lee.

6 recordsLinked to original sources

arXiv2026

Defect-controlled twin activation in crystallographically equivalent magnesium micropillars

Tensile twinning plays a central role in accommodating -axis plasticity in Mg. In bulk Mg, it typically shows a relatively deterministic response with a low critical stress, whereas in confined volumes it exhibits broad yield-stress distributions that complicate the prediction of small-scale mechanical behavior. Here, site-specific compression tests are performed on 4 $μ$m-diameter pillars fabricated in a parent Mg crystal and an adjacent {10-12} twin. The two regions share the same [11-20] compression axis but experienced different prior deformation histories, allowing the influence of the residual microstructural state to be examined at fixed crystallographic orientation. Among 27 pillars, most parent-region pillars yield near 300 MPa, whereas pillars from the twin region span approximately 30 to 300 MPa. Interrupted tests combined with cross-sectional EBSD link individual load drops to discrete twin formation and further show that a pillar containing a pre-existing twin yields at approximately 80 MPa through the migration of the existing twin boundary. Molecular dynamics simulations of 30 nm-diameter pillars resolve possible atomistic pathways at the nanoscale. The simulations illustrate how contact geometry and pre-existing twin embryos alter event selection, and how an activated twin advances rapidly, while coherent twin boundary migration proceeds through disconnection motion accompanied by crystallographically required atomic shuffles. The results attribute the experimental scatter to the local availability of embryos and mobile interfaces, such that the first plastic event is governed by the twinning pathway accessible from the local microstructural state rather than by a single characteristic critical stress. Deformation history can therefore strongly modify the distribution of first plastic events even when the loading orientation is fixed.

cond-mat.mtrl-sci↗

arXiv2026

Zonal dislocations in Laves phases: A coupled synchro-shear slip mechanism

Synchro-shear is the primary plastic deformation mechanism in Laves phases at elevated temperatures, mediated by synchro-Shockley partial dislocations-zonal dislocations that proceed via localized events such as kink-pair nucleation and propagation. Using atomistic simulations, we identified a novel slip mechanism in Laves phases, namely coupled synchro-shear slip, involving the synchronized glide of two synchro-Shockley partial dislocations on adjacent slip planes, leading to the formation of extrinsic stacking faults. High-resolution scanning transmission electron microscopy revealed the extended core structures consistent with coupled synchro-Shockley partial dislocations bounded by extrinsic stacking faults in C15 NbCr2 and their involvement in twinning. These results highlight the critical role of coupled synchro-shear slip in enabling phase transformations between Laves polytypes and in governing twinning behavior, providing new atomistic insight into the kinetic nature of plasticity in topologically close-packed intermetallic phases.

cond-mat.mtrl-sci↗

arXiv2025

Towards Defect Phase Diagrams: From Research Data Management to Automated Workflows

Defect phase diagrams provide a unified description of crystal defect states for materials design and are central to the scientific objectives of the Collaborative Research Centre (CRC) 1394. Their construction requires the systematic integration of heterogeneous experimental and simulation data across research groups and locations. In this setting, research data management (RDM) is a key enabler of new scientific insight by linking distributed research activities and making complex data reproducible and reusable. To address the challenge of heterogeneous data sources and formats, a comprehensive RDM infrastructure has been established that links experiment, data, and analysis in a seamless workflow. The system combines: (1) a joint electronic laboratory notebook and laboratory information management system, (2) easy-to-use large-object data storage, (3) automatic metadata extraction from heterogeneous and proprietary file formats, (4) interactive provenance graphs for data exploration and reuse, and (5) automated reporting and analysis workflows. The two key technological elements are the openBIS electronic laboratory notebook and laboratory information management system, and a newly developed companion application that extends openBIS with large-scale data handling, automated metadata capture, and federated access to distributed research data. This integrated approach reduces friction in data capture and curation, enabling traceable and reusable datasets that accelerate the construction of defect phase diagrams across institutions.

cs.DB↗

arXiv2025

Chemically tailored planar defect phases in the Ta-Fe μ-phase

Intermetallics often exhibit complex crystal structures, which give rise to intricate defect structures that critically influence their mechanical and functional properties. Despite studies on individual defect types, a comprehensive understanding of the defect landscape in μ-phases, a class of topologically close-packed phases, remains elusive. In this study, we investigated the planar defect structures in the Ta-Fe μ-phase across a compositional range of 46 to 58 at.% Ta using electron microscopy and density functional theory calculations. Electron backscatter diffraction and high-resolution scanning transmission electron microscopy reveal a transition from basal twin boundaries and planar faults containing C14 TaFe2 Laves phase layers at a low Ta content to pyramidal {1\bar{1}02} twins at a higher Ta content. Density functional theory calculations of defect formation energies confirm a chemical potential-driven stabilisation of Laves phase lamellae. The prevalence of pyramidal twins in Ta-rich μ-phase samples is attributed to the competitive nature of different planar defects during solidification. A defect landscape for μ-phases is proposed, illustrating the interplay between site occupancy, dislocation types and planar faults across the chemical potential space. These findings provide fundamental insights into defect engineering in structurally complex intermetallics and open pathways for optimising material properties through chemical tuning.

cond-mat.mtrl-sci↗

arXiv2024

Automated Segmentation of Large Image Datasets using Artificial Intelligence for Microstructure Characterisation, Damage Analysis and High-Throughput Modelling Input

Many properties of commonly used materials are driven by their microstructure, which can be influenced by the composition and manufacturing processes. To optimise future materials, understanding the microstructure is critically important. Here, we present two novel approaches based on artificial intelligence that allow the segmentation of the phases of a microstructure for which simple numerical approaches, such as thresholding, are not applicable: One is based on the nnU-Net neural network, and the other on generative adversarial networks (GAN). Using large panoramic scanning electron microscopy images of dual-phase steels as a case study, we demonstrate how both methods effectively segment intricate microstructural details, including martensite, ferrite, and damage sites, for subsequent analysis. Either method shows substantial generalizability across a range of image sizes and conditions, including heat-treated microstructures with different phase configurations. The nnU-Net excels in mapping large image areas. Conversely, the GAN-based method performs reliably on smaller images, providing greater step-by-step control and flexibility over the segmentation process. This study highlights the benefits of segmented microstructural data for various purposes, such as calculating phase fractions, modelling material behaviour through finite element simulation, and conducting geometrical analyses of damage sites and the local properties of their surrounding microstructure.

cond-mat.mtrl-sci↗

arXiv2023

Three-Dimensional Damage Characterisation in Dual Phase Steel using Deep Learning

High performance sheet metals with a multi-phase microstructure suffer from deformation induced damage formation during forming in the constituent phases but importantly also where these intersect. To capture damage in terms of the physical processes in three dimensions (3D) and its stochastic nature during deformation, two challenges remain to be tackled: First, bridging high resolution analysis towards large scales to consider statistical data and, second, characterising in 3D with a resolution appropriate for sub-micron sized voids at a large scale. Here, we present how this can be achieved using panoramic scanning electron microscopy (SEM), metallographic serial sectioning, and deep-learning assisted automatic image analysis. This brings together the 3D evolution of active damage mechanisms with volumetric and environmental information for thousands of individual damage sites. We also assess potential surface preparation artefacts in 2D analyses. Overall, we find that for the material considered here, a dual phase (DP800) steel, martensite cracking is the dominant but not sole origin of deformation induced damage and that for a quantitative comparison of damage density, metallographic preparation can induce additional surface damage density far exceeding what is commonly induced between uniaxial straining steps.

cond-mat.mtrl-sci↗