arXiv subjects

Talal Al-Samman

Publications and source records attributed to Talal Al-Samman.

9 recordsLinked to original sources

arXiv2026

Stochastic twinning in confined volumes of Mg: Insights from in-situ micromechanical testing and atomistic simulations

Tensile twinning plays a central role in accommodating -axis plasticity in Mg. In bulk Mg, twinning typically shows a relatively deterministic response with a low critical stress, whereas in confined volumes it exhibits pronounced scatter, complicating the prediction of small-scale mechanical behavior. In this study, we investigate the origin of this stochasticity by combining site-specific micropillar compression with atomistic simulations. Experiments show that under -axis compression, plastic deformation is dominated by {10-12} twinning, with each discrete stress drop in the stress-strain response marking the activation and rapid advance of a twin. Atomistic simulations further separate twinning into two mechanistic regimes: nucleation and longitudinal propagation occur in a high-stress, shuffle-assisted regime, whereas lateral thickening proceeds in a low-stress regime controlled by disconnection glide. Linking these mechanistic insights with post-mortem characterization of deformed pillars demonstrates that the scatter in measured yield stresses arises from stochastic selection among competing twinning pathways, governed by the local defect landscape (presence, distribution, and morphology of pre-existing defects). Overall, this work identifies an atomistic basis for size-dependent stochastic twinning in Mg and provides a general framework for materials whose plasticity is controlled by discrete activation events.

cond-mat.mtrl-sci↗

arXiv2024

Resolution Enhancement of Scanning Electron Micrographs using Artificial Intelligence

Scanning Electron Microscopy (SEM) is pivotal in revealing intricate micro- and nanoscale features across various research fields. However, obtaining high-resolution SEM images presents challenges, including prolonged scanning durations and potential sample degradation due to extended electron beam exposure. This paper addresses these challenges by training and applying a deep learning based super-resolution algorithm. We show that the chosen algorithm is capable of increasing the resolution by a factor of 4, thereby reducing the initial imaging time by a factor of 16. We benchmark our method in terms of visual similarity and similarity metrics on two different materials, a dual-phase steel and a case-hardening steel, improving over standard interpolation methods. Additionally, we introduce an experimental pipeline for the study of rare events in scanning electron micrographs, without losing high-resolution information.

eess.IV↗

arXiv2024

Grain boundary segregation spectrum in basal-textured Mg alloys: From solute decoration to structural transition

Mg alloys are promising lightweight structural materials due to their low density and excellent mechanical properties. However, their limited formability and ductility necessitate improvements in these properties, specifically through texture modification via grain boundary segregation. While significant efforts have been made, the segregation behavior in Mg polycrystals, particularly with basal texture, remains largely unexplored. In this study, we performed atomistic simulations to investigate grain boundary segregation in dilute and concentrated solid solution Mg-Al alloys. We computed the segregation energy spectrum of basal-textured Mg polycrystals, highlighting the contribution from specific grain boundary sites, such as junctions, and identified a newly discovered bimodal distribution which is distinct compared to the conventional skew-normal distribution found in randomly-oriented polycrystals. Using a hybrid molecular dynamics/Monte Carlo approach, we simulated segregation behavior at finite temperatures, identifying grain boundary structural transitions, particularly the varied fraction and morphology of topologically close-packed grain boundary phases when changing thermodynamic variables. The outcomes of this study offer crucial insights into basal-textured grain boundary segregation and phase formation, which can be extended to other relevant Mg alloys containing topologically close-packed intermetallics.

cond-mat.mtrl-sci↗

arXiv2024

Predicting Grain Boundary Segregation in Magnesium Alloys: An Atomistically Informed Machine Learning Approach

Grain boundary (GB) segregation in magnesium (Mg) substantially influences its mechanical properties and performance. Atomic-scale modelling, typically using ab-initio or semi-empirical approaches, has mainly focused on GB segregation at highly symmetric GBs in Mg alloys, often failing to capture the diversity of local atomic environments and segregation energies, resulting in inaccurate structure-property predictions. This study employs atomistic simulations and machine learning models to systematically investigate the segregation behavior of common solute elements in polycrystalline Mg at both 0 K and finite temperatures. The machine learning models accurately predict segregation thermodynamics by incorporating energetic and structural descriptors. We found that segregation energy and vibrational free energy follow skew-normal distributions, with hydrostatic stress, an indicator of excess free volume, emerging as an important factor influencing segregation tendency. The local atomic environment's flexibility, quantified by flexibility volume, is also crucial in predicting GB segregation. Comparing the grain boundary solute concentrations calculated via the Langmuir-McLean isotherm with experimental data, we identified a pronounced segregation tendency for Nd, highlighting its potential for GB engineering in Mg alloys. This work demonstrates the powerful synergy of atomistic simulations and machine learning, paving the way for designing advanced lightweight Mg alloys with tailored properties.

cond-mat.mtrl-sci↗

arXiv2023

Solute Co-Segregation Mechanisms at Low-Angle Grain Boundaries in Magnesium: A Combined Atomic-Scale Experimental and Modeling Study

Solute segregation at low-angle grain boundaries (LAGBs) critically affects the microstructure and mechanical properties of magnesium (Mg) alloys. In modern alloys containing multiple substitutional elements, understanding solute-solute interactions at microstructural defects becomes essential for alloy design. This study investigates the co-segregation mechanisms of calcium (Ca), zinc (Zn), and aluminum (Al) at a LAGB in a dilute AZX010 Mg alloy by combining atomic-scale experimental and modeling techniques. Three-dimensional atom probe tomography (3D-APT) revealed significant segregation of Ca, Zn, and Al at the LAGB, with Ca forming linear segregation patterns along dislocation arrays characteristic of the LAGB. Clustering analysis showed increased Ca-Ca pairs at the boundary, indicating synergistic solute interactions. Atomistic simulations and elastic dipole calculations demonstrated that larger Ca atoms prefer tensile regions around dislocations, while smaller Zn and Al atoms favor compressive areas. These simulations also found that Ca-Ca co-segregation near dislocation cores is energetically more favorable than other solute pairings, explaining the enhanced Ca clustering observed experimentally. Thermodynamic modeling incorporating calculated segregation energies and solute-solute interactions accurately predicted solute concentrations at the LAGB, aligning with experimental data. The findings emphasize the importance of solute interactions at dislocation cores in Mg alloys, offering insights for improving mechanical performance through targeted alloying and grain boundary engineering.

cond-mat.mtrl-sci↗

arXiv2022

Atomistic insights into the inhomogeneous nature of solute segregation to grain boundaries in magnesium

In magnesium alloys with multiple substitutional elements, solute segregation at grain boundaries (GBs) has a strong impact on many important material characteristics, such as GB energy and mobility, and therefore, texture. Although it is well established that GB segregation is inhomogeneous, the variation of GB solute composition for random boundaries is still not understood. In the current study, atomic-scale experimental and simulation techniques were used to investigate the compositional inhomogeneity of six different GBs. Three-dimensional atom probe tomography results revealed that GB solute concentration of Nd in Mg varies between 2 to 5 at.%. This variation was not only seen for different GB orientations but also within the GB plane. Correlated atomistic simulations suggest that the inhomogeneous segregation behavior observed experimentally stems from local atomic rearrangements within the GBs and introduce the notion of potential excess free volume in the context of improving the prediction of per-site segregation energies.

cond-mat.mtrl-sci↗

arXiv2020

Prediction of void evolution in sheet bending based on statistically representative microstructural data for the Gurson-Tvergaard-Needleman model

Ductile damage in sheet steels is caused by voids. It is crucial for product design to predict the distribution of voids in bent components. Since the void volume fraction is a state variable in the Gurson-Tvergaard-Needleman (GTN) model, it is applied to predict the evolution of voids in bending. Material parameters are identified based on force-displacement curves of a dual phase steel and also through statistical microstructural information obtained from panoramic scanning-electron microscopy images. The void volume fraction and particular void populations of GTN-model are determined with a recently proposed scheme, which involves machine learning algorithms.

cond-mat.mtrl-sci↗

arXiv2020

On the effect of strain and triaxiality on void evolution in a heterogeneous microstructure -- A statistical and single void study of damage in DP800

In order to improve the understanding of damage evolution in mechanically heterogeneous microstructures, like the ones of dual-phase steels, the influence of the applied stress state is a key element. In this work, we studied the influence of the globally applied stress state on the evolution of damage in such a microstructure. Classical damage models allow predictions of damage during deformation based on considerations of the material as an isotropic continuum. Here, we investigate their validity in a dual phase microstructure that is locally dominated by its microstructural morphological complexity based on a statistical ensemble of thousands of individual voids formed under different stress states. For this purpose, we combined a calibrated material model incorporating damage formation to assess the local stress state in samples with different notch geometries and high-resolution electron microscopy of large areas using a deep learning-based automated micrograph analysis to detect and classify microstructural voids according to their source of origin. This allowed us to obtain both the continuum stress state during deformation and statistically relevant data of individual void formation. We found that the applied plastic strain is the major influence on the overall number, and therefore the nucleation of new voids, while triaxiality correlates with the median void size, supporting its proposed influence on void growth. In contrast, coalescence of voids leading to failure appears related to local instabilities in the form of shear band formation and is therefore only indirectly determined by the global stress state in that it determines the global distribution, density and size of voids.

cond-mat.mtrl-sci↗

arXiv2019

High-resolution, yet statistically relevant, analysis of damage in DP steel using artificial intelligence

High performance materials, from natural bone over ancient damascene steel to modern superalloys, typically possess a complex structure at the microscale. Their properties exceed those of the individual components and their knowledge-based improvement therefore requires understanding beyond that of the components' individual behaviour. Electron microscopy has been instrumental in unravelling the most important mechanisms of co-deformation and in-situ deformation experiments have emerged as a popular and accessible technique. However, a challenge remains: to achieve high spatial resolution and statistical relevance in combination. Here, we overcome this limitation by using panoramic imaging and machine learning to study damage in a dual-phase steel. This high-throughput approach not only gives us strain and microstructure dependent insights across a large area of this heterogeneous material, but also encourages us to expand current research past interpretation of exemplary cases of distinct damage sites towards the less clear-cut reality.

cond-mat.mtrl-sci↗