Abstract Reliable surface area estimation remains a practical bottleneck in three-dimensional voxelbased microstructural models, particularly in metallurgical problems governed by interfacial phenomena. While grain volume can be computed directly from voxel counts with minimal ambiguity, surface measures derived from digital lattices are not unique and often exhibit systematic bias and orientation dependence. This work proposes a calibrated restricted-face framework for grain surface area estimation in 3D voxel-based growth models. Three surface definitions are examined: (i) equivalent surface area inferred from volume under a spherical assumption, (ii) full-face surface area based on exposed voxel faces, and (iii) a restricted-face proxy derived from 6-neighborhood surfacevoxel classification. The restricted-face approach provides an orientation-robust and computationally efficient estimator of interfacial extent, with its systematic underestimation corrected through a single dimensionless proportionality constant obtained from analytical benchmarks. Validation against analytical solutions for spheres and spheroids in 201³ lattices shows that corrected estimates achieve deviations on the order of ±10% across the tested size and orientation ranges. Application to polycrystalline simulations in 200³ lattices with 1,000 randomly distributed nuclei yields statistically consistent surface-area distributions and captures the expected increase in interfacial measures under anisotropic growth. Overall, the proposed framework provides a practical and scalable solution for surface-dependent analyses in voxel-based microstructural simulations.
Abstract The full elastic constant tensor and the surface energies of five low-index planes—(100), (101), (110), (111), and (001)—of β -Sn (tetragonal, I4 1 /amd) are calculated using density functional theory (DFT) with the Perdew–Burke–Ernzerhof (PBE) functional and a universal machine-learning interatomic potential (MLIP; Preferred Potential, PFP v8), which provides both PBE and regularized-restored strongly constrained and appropriately normed (r 2 SCAN) calculation modes, each with and without Grimme’s D3 dispersion correction. Results are compared with three modified embedded-atom method (MEAM) potentials and experimental data. Both DFT and PFP resolve the long-standing order-of-magnitude C 44 deficiency of MEAM potentials (1.5–7.9 GPa against the experimental 22.0 GPa), yielding C 44 = 17.9–34.3 GPa. In contrast, C 12 is systematically underestimated by all first-principles-based methods (15.2–41.5 GPa against 59.4 GPa) regardless of the choice of exchange–correlation functional, indicating a limitation of current functionals rather than an artifact of MLIP training. For surface energies, DFT/PBE predicts (100) to be the lowest-energy plane of β -Sn, and six of the seven non-DFT methods reproduce this. The equilibrium Wulff shapes of all methods except one MEAM potential retain a rounded polyhedral character qualitatively similar to that of DFT/PBE. Among the non-DFT methods, PFP/r 2 SCAN reproduces the DFT/PBE surface energies most closely. No single method simultaneously reproduces both the full elastic constant tensor and the surface-energy anisotropy of β -Sn, and the choice of computational method should be guided by the specific phenomenon of interest; among the methods tested, PFP/PBE + D3 offers the smallest mean absolute percentage error (MAPE) of 25.1% for the elastic constants with respect to experiment, while PFP/r 2 SCAN gives the smallest MAPE of 5.0% for the surface energies with respect to DFT/PBE.
Abstract Numerical simulation of interface evolution in multiphase and multimaterial systems requires accurate estimation of geometric quantities such as interface normals and curvature, which govern the kinetics of the underlying physical processes. While several curvature estimation techniques exist, such as the height-function method, they generally require continuous volume fraction information and are not directly applicable to binarized fields, where volume fractions are represented by discrete values of 0 or 1. Such binary representations frequently arise in cellular automata (CA) simulations and segmented image data. In these cases, conventional counting-based approaches provide only a limited representation of the underlying interface geometry, often leading to reduced accuracy. In this work, we propose a machine-learning-based framework for curvature estimation in binarized fields as an alternative to traditional counting cell approaches. A neural network is first trained using continuous volume fraction fields generated from known interface geometries. The model is subsequently adapted to binarized fields through a transfer-learning strategy, and its performance is compared against a network trained directly on binarized data. The accuracy of the proposed approach is assessed using standard benchmark geometries, along with the estimates of bias originating from the simplification to binarized field data. Finally, the methodology is integrated into a CA interface-evolution framework to demonstrate curvature-driven microstructural evolution in an analytically verifiable single-grain setting and a multi-grain environment. The results demonstrate that the proposed approach provides accurate curvature estimates and reproduces the expected interface-evolution and grain-growth kinetics in binarized systems.
Abstract In this work, an efficient asymptotic expansion homogenization (AEH) framework is developed with a preconditioned conjugate gradient fast Fourier transform (CG-FFT) spectral solver for temperature-dependent nonlinear thermal homogenization. The standard AEH cell problems are extended to account for nonlinear constitutive behavior, which are then solved iteratively by a consistent linearization technique. An adaptive Poisson preconditioner, with a reference conductivity automatically selected via residual probing and updated with temperature, markedly improves the conditioning of the diffusion operator and accelerates solver convergence. The finite element method (FEM)-based AEH framework is implemented in Abaqus for robust validation. A three-dimensional representative volume element (RVE) is considered while varying the material contrast and nonlinear responses. The results indicate that embedding the CG-FFT solver into the conventional AEH cell problem reduces computational cost at higher accuracy. This study provides a computationally efficient route for temperature-dependent multiscale thermal analysis of high-resolution voxel-based RVEs, while maintaining the mathematical rigor of AEH. The framework sets the foundation for future advancements to fully coupled thermomechanical analysis with interfacial resistance and graphics processing units.
Abstract A microstructure-resolved electro-chemo-mechanical framework is developed to investigate strengthening and stress corrosion cracking (SCC) in laser powder bed fusion (LPBF) 316L stainless steel reinforced with Al 2 O 3 ceramic nanoparticles. The framework, implemented in the MOOSE finite element platform, couples three physics: finite-strain crystal plasticity for the austenitic matrix, linear elasticity with a Rankine upper-bound stress limit criterion for ceramic inclusions, and a thermodynamically consistent Allen–Cahn phase-field model for SCC evolution. Electrochemical dissolution is described through Butler–Volmer kinetics, while the interaction between mechanical deformation and corrosion is incorporated through a slip-dependent interfacial mobility that represents passive-film rupture and accelerated local dissolution. Simulations are performed on two-dimensional representative microstructures of pure LPBF 316L and Al 2 O 3 reinforced nanocomposites. Mechanical results show that Al 2 O 3 particles impose kinematic constraints that force crystallographic slip to localize within confined matrix ligaments, increasing kinematic efficiency and accelerating strain hardening through an Orowan strengthening mechanism. SCC simulations demonstrate that reinforcement delays the pit-to-crack transition, reduces degradation depth, and alters crack morphology through particle shielding, crack deflection, and localized branching. These results demonstrate that particle size, content, and spatial distribution play critical roles in governing the balance between mechanical strengthening and SCC resistance in LPBF 316L nanocomposites.
Abstract Nanoindentation with a Knoop indenter tip can reveal the plastic response of brittle materials, which is contained in the measured Knoop hardness anisotropy and force–displacement curves. Due to short length scales, details of the local dislocation distribution and dislocation stresses cannot be ignored in the analysis of experimental measurements. Inclusion of such effects leads to a non-local theory of plasticity, where gradients of strain fields are present in the expressions for stress. In this work, a non-local plasticity model is developed and implemented in the Abaqus finite element software. The geometrically necessary dislocations (GND) are quantified via the Nye tensor, and the backstress tensor is found from gradients of the Nye tensor by summation of dislocation stresses. Micro- and nanoindentation experiments on pentaerythritol tetranitrate (PETN) are simulated with the developed model. The average errors between simulated and measured micro- and nanohardness are around 13% and 25%, respectively, while the maximum errors are around 20% and 30%, respectively. The set of active slip systems for PETN that can match the experimental hardness anisotropy trends is { 110 } ⟨ 1 1 ¯ 1 ⟩ , { 100 } ⟨ 011 ⟩ , and { 101 } ⟨ 10 1 ¯ ⟩ . The GND hardening, the backstress, and the indenter shape are investigated in relation to the indentation size effects. The model predicts a weak effect of backstress on hardness, while the coupled effects of indenter shape and GND hardening are predominantly responsible for predicted size effects.
Abstract Machine learning (ML) techniques have become pivotal in material design, yet accurately capturing the subtle energy shifts associated with local chemical order (LCO) in multi-principal element alloys remains a significant challenge. This study establishes a graph convolutional neural network (GCNN) framework specifically engineered to map the potential energy landscape of NiCoCr medium-entropy alloys with high sensitivity to LCO transitions. Utilizing hybrid Monte-Carlo molecular dynamics simulations as a systematic evaluation benchmark, we evaluate the GCNN’s capacity to learn the non-linear relationship between atomic arrangements and structural stability across varying thermal regimes. The atomic configurations are transformed into graph representations, where nodes incorporate atom types and absolute velocities, and edges are established with the 12 nearest neighbours to encapsulate the local chemical environment. The model’s fitting and predictive fidelity was assessed through three distinct case studies: individual thermal datasets, combined temperature ranges, and unseen configurations. The GCNN demonstrates exceptional performance, achieving coefficient of determination R 2 values up to 0.98 and a mean absolute error as low as 1.04 meV atom −1 on entirely withheld thermal trajectories (550 K), effectively tracking the energy variations correlated with the system’s LCO evolution. This research offers a comprehensive graph-based modelling approach for understanding the configuration-to-energy mapping relationships in complex multi-principal element alloys, providing a baseline structural architecture that can be extended toward ML interatomic potential workflows.
Abstract In this study, the one-dimensional solar capacitance simulator (SCAPS-1D) was used to simulate an environmentally benign perovskite solar cell architecture. The simulation analysis focused on the investigation of the absorber layer thickness, acceptor concentration, and defect density on photovoltaic properties. Furthermore, it examined the impact of interface defects at the PEDOT:PSS/CsRbSnI 3 and CsRbSnI 3 /WS 2 interfaces. The SCAPS-1D simulation results indicate that the optimal conditions for high photovoltaic performance include an absorber thickness of 1 μ m , an acceptor concentration of 10 16 cm − 3 , and a defect density of 8.5 × 10 14 cm − 3 . Additionally, the results show that defects at the CsRbSnI 3 /WS 2 interface have a more pronounced effect on solar cell performances compared to the PEDOT:PSS/CsRbSnI 3 interface. The conduction and valence band offsets are −0.32 eV and 0.05 eV, respectively, and play a significant role in determining device performance. Moreover, the photovoltaic properties were further investigated by considering various transport layers. The ETL include PCBM, ZnO, TiO 2 , C 60 , IGZO, STO, WS 2 , and WO 3 , while the HTL include Cu 2 O, CuSCN, CBTS, PEDOT:PSS, Spiro-OMeTAD, rGO, MoO 3 , and CNTs. Among the simulated heterostructures, the champion device exhibited the configuration FTO/WS 2 /CsRbSnI 3 /PEDOT:PSS/Au, achieving a power conversion efficiency (PCE) of 24.07%, with an open-circuit voltage ( V oc ) of 0.880 V, a short-circuit current density ( J sc ) of 32.894 mA cm − 2 , and a fill factor of 83.09%.
Abstract This study reviews the major damage modelling strategies used in finite element (FE) analysis of bone fracture. The literature was surveyed with an emphasis on macro-scale (whole-bone) studies, excluding nano-, micro-, and meso-scale models to maintain focus on organ-level fracture behaviour. The damage modelling approaches discussed consist of both continuum methods, namely damage-induced stiffness degradation, elastoplastic models, and the phase-field method (PFM), and discrete methods, namely the cohesive zone method (CZM) and the extended FE method (XFEM). The comparative analysis shows that damage-induced stiffness degradation and elastoplastic models simulate progressive damage accumulation and permanent deformation within elements, while CZM explicitly represents discrete crack propagation along predefined interfaces and thus requires prior knowledge of likely crack paths. In contrast, PFM and XFEM can model crack initiation and propagation without needing predefined paths, albeit at substantially higher computational costs. Although the main focus of this review is on damage-related mechanical properties, it also considers bone density and elasticity, which are factors that influence simulation outcomes. By comparing the strengths, limitations, and typical applications of each modelling approach, this review aims to guide method selection, provide ranges of mechanical properties, and highlight how combining different approaches can improve both accuracy and efficiency in bone fracture FE simulations.
The design of materials for oil and gas extraction is critical to the long-term safe operation of downhole equipment and the exploitation of deep-sea and unconventional resources. MgNi alloys exhibit high strength and excellent corrosion resistance, but their unsatisfactory mechanical performance, such as low stiffness and inherent brittleness, restricts practical applications and processing. In this work, a deep potential (DP) model for MgNi alloys was developed within the AI for Science framework to investigate the effects of composition and point defects on crystallographic, mechanical, and corrosion behaviors. The DP model achieves high accuracy, with root-mean-square errors values of 1.48 & times; 10-3 eV/atom for energy and 3.47 & times; 10-3 eV & Aring;-1 for atomic forces. Predicted lattice constants and mechanical properties agree well with density functional theory and experimental results, with deviations within 0.5%. Key energy parameters are reproduced with relative errors below 1%, demonstrating reliable simulation capability for corrosion dissolution kinetics. Overall, the proposed DP model provides an efficient and accurate tool for performance optimization and reliability design of MgNi alloys, and offers a robust basis for multiscale modeling and inverse design of high-performance alloys.
Abstract Perovskite-based tandem solar cells offer a promising route for efficient solar energy conversion across a broad spectrum. In this study, we explore the development of perovskite-based tandem solar cells, focusing on Cs 3 Bi 2 I 9 and La 2 NiMnO 6 (LNMO) as absorber layers, in conjunction with FTO as the transparent conducting oxide. Initial investigations utilizing only LNMO and Cs 3 Bi 2 I 9 with FTO yielded an efficiency of 22.4%, which was subsequently optimized to achieve an efficiency of 31.17% through the integration of electron transport layers (ETLs) and hole transport layers (HTLs). Notably, the incorporation of titanium dioxide (TiO 2 ) as the ETL, in conjunction with various HTLs such as poly[bis(4-phenyl)(2,4,6-trimethylphenyl)amine] (PTAA), resulted in the highest efficiency. Further investigations into interlayers revealed their significant impact on device performance, with certain combinations enhancing efficiency while others exhibited a decrease. Additionally, suitable back contacts were compared, and the performance of the configured tandem solar cell was analyzed under diverse conditions. This work presents a comprehensive one-dimensional solar cell capacitance simulator (SCAPS-1D) simulation study focused on optimizing a fully lead-free tandem solar cell that combines Cs₃Bi₂I₉ and La₂NiMnO₆ absorber layers. The theoretical predictions derived from this study represent the upper efficiency limits achievable under idealized simulation conditions, providing valuable insights and guidance for future experimental design and optimization of sustainable, lead-free perovskite-based tandem solar cell devices. Meticulous simulations using SCAPS-1D affirmed the viability of the Cs 3 Bi 2 I 9 /LNMO-based tandem solar cells, reaching optimal efficiency. This research contributes valuable insights into the optimization of device architecture and interface engineering, paving the way for the development of efficient and cost-effective solar energy harvesting systems.
Abstract To overcome the limited spectral absorption and carrier generation in single absorber layers, the concept of double absorber perovskite solar cells (DAPSCs) has emerged, wherein two complementary perovskite materials are stacked to enhance light harvesting and improve overall photovoltaic (PV) performance. This study presents a numerical investigation of a lead-free, DAPSC utilizing environmentally friendly inorganic perovskite materials, with a focus on parameter optimization. The proposed PSC structure incorporates Rb 2 LiGaBr 6 and Cs 2 AgBiI 6 as absorber layers, tungsten disulfide (WS 2 ) as the electron transport layer (ETL), and molybdenum trioxide (MoO 3 ) as the hole transport layer (HTL). To determine the optimal configuration, various HTLs including Cu 2 O, CBTS, MoO 3 , Cu:NiO, Sb 2 S 3 , MoTe 2 , PEDOT:PSS, and CFTS are evaluated alongside ETLs such as ZnOS, CdZnS, WS 2 , In 2 S 3 , TiO 2 , ZnSe, and CdS. SCAPS-1D software was employed to systematically assess the effects of absorber layer thickness, HTL and ETL thickness, doping concentration, defect density, and back-contact metal work function on key photovoltaic performance metrics, including power conversion efficiency (PCE), short-circuit current density ( J SC ), open-circuit voltage ( V OC ), and fill factor (FF). Additionally, the study examines the influence of series and shunt resistance, and operating temperature. The optimized PSC configuration, comprising a Cs 2 AgBiI 6 thickness of 100 nm, Rb 2 LiGaBr 6 thickness of 1900 nm, and a defect density of 10 15 cm − 3 , achieves a J SC of 39.454 mA cm − 2 , a V OC of 0.928 V, an FF of 83.062%, and a PCE of 30.40%. These findings provide valuable insights for the advancement of high-performance, lead-free PSC designs, contributing to the development of environmentally sustainable photovoltaic technologies.
Abstract Material response of metals and alloys depends on a wide range of factors ranging from the material’s composition and point defects, to larger defects such as dislocations and/or precipitates, and even aspects such as the grain size, texture or other microstructural factors. Furthermore, how a material is processed and then subsequently loaded also has an effect. Combined, these aspects span length and time scales that vary by several orders of magnitude, providing a grand challenge to any single modelling approach. Consequently, over the past decades, multiscale modelling has emerged as a foundational tool for investigating and predicting the performance of metals and alloys. While much development has been done, there are still open challenges in how methods bridge across different time and/or length scales, how we inform, validate, and understand complex material behaviour, and more recently how emerging machine learning and artificial intelligence tools can enhance our multiscale modelling approaches. Addressing these challenges and the outlook for multiscale modelling on the horizon is the focus of this roadmap article. The contributions within are divided broadly into three categories: bridging the length scale gap between models, targeting timescale gaps alongside length scale gaps, and bridging the gap between experiment and simulation.
Residual stress and distortion remain critical challenges in the laser powder bed fusion (L-PBF) additive manufacturing of complex load-bearing components such as spur gears, particularly for high-performance alloys like Inconel 718 (IN718). In this study, a comprehensive numerical-experimental framework is developed to investigate the evolution of residual stress and deformation in an L-PBF-manufactured IN718 spur gear, with explicit consideration of process parameters, thermal history, and geometric constraints. A thermo-mechanical simulation strategy was implemented using ansys additive print to model layer-wise heat transfer, stress evolution, and distortion. Two contrasting parameter sets were considered, involving variations in laser power, layer thickness, scan speed, scan orientation, and bed temperature. Spatial distributions of normal and shear stress components, Von Mises equivalent stress, and maximum deformation were analyzed along the build height to elucidate stress polarity, accumulation mechanisms, and distortion sensitivity. Simulation results reveal a pronounced transition from a compression-dominated residual stress field under higher energy input and elevated bed temperature to a tensile-dominated stress state under reduced thermal buffering and orthogonal scan rotation. The adverse parameter set resulted in a 10.5% increase in peak residual stress and nearly a fivefold rise in deformation, highlighting the compounded influence of layer thickness, scan orientation, and substrate temperature. Correlation analysis further demonstrates that build-direction stress components strongly govern residual stress accumulation, while deformation shows limited coupling with in-plane stresses. Experimental validation was conducted on an IN718 spur gear fabricated using optimized parameters on an EOS M280 system. X-ray diffraction (sin2 psi) measurements confirmed a compressive surface residual stress state (588 MPa), closely aligning with the numerically predicted low-stress regime. Dimensional inspection indicated excellent geometric fidelity, with a maximum deviation of 0.20 mm. Comparative stress-deformation performance mapping demonstrates that the experimental build occupies a favorable low-stress, low-distortion domain, validating the predictive capability of the simulation framework. The proposed integrated methodology provides robust insights for process parameter selection and offers a reliable pathway for residual stress mitigation and distortion.
Abstract Nickel–titanium (Ni–Ti) alloys are predominantly known for their shape memory applications. However, the Ni–Ti system also exhibits complex phases, especially in the Ni-rich compositions, which can be exploited to design high-performance alloys. A critical step in this process is determining the phase morphology and microstructure evolution. The first-principles phase field (FPPF) method is an excellent choice due to its ability to predict the microstructures at a low computational cost. This work utilizes the FPPF method to study the microstructural evolution in Ni-rich compositions from 58–90 at.% Ni. The compositions between 87–90 at.% Ni are used to examine coarsening kinetics under the Lifshitz, Slyozov, and Wagner theory and the trans-interface diffusion-controlled theory. These results are compared with experimental observations in the literature to validate the FPPF method. This method is further applied to study the microstructure evolution in unexplored alloys between 58–80 at.% Ni.
Abstract Machine learning (ML) has emerged as a transformative approach for overcoming the unfavorable scaling that limits system sizes and accessible timescales in electronic structure calculations, which underpin modern computational sciences. Herein, we provide a comprehensive examination of ML methods that learn electronic structure representations across multiple levels of theory. We introduce an ‘ML Jacob’s ladder’ framework that categorizes methods by the completeness of electronic structure information they capture, from determining the many-body wavefunction through neural quantum states, to parameterizing exchange- correlation functionals in density functional theory, to predicting Hamiltonians and Fock matrices that bypass self-consistent-field iterations, and finally to learning electron densities from which diverse observables can be derived. Throughout, we emphasize the conceptual unification of these approaches through diverse representations and shared physical principles such as the variational theorem, Kohn–Sham formalism, and hierarchical corrections that make ML uniquely suited for this domain. We also highlight generalizable models emerging across rungs that promise transferability between chemical systems and levels of theory, alongside practical implementations for real molecular and materials systems. By examining how different architectural choices and physical constraints shape model capabilities, we map the evolving landscape where ML is reshaping the accessibility, accuracy, and interpretability of quantum mechanical predictions.
The presence of point defects in crystalline structures are known to change the properties of materials. They concomitantly largely modify the local structure without affecting the long-range order signature (x-ray diffraction pattern). We investigate, using molecular dynamics simulations, the influence of the presence of Gd3+ doping cation and oxygen vacancies point defects on the short-range order of the CeO2 fluorite structure. These defects are introduced following two arrangements the Random (R) and the Neighbour (N). We first study the evolution with the temperature and the doping ratio of the M-O-M and O-O (M = Ce, Gd) bond angle distributions. Different trends are noted according to the point defect configuration (R or N) on the angle thermal expansion mainly for the Gd-O-Gd angle. The analysis of the average cation coordination number reveals that the oxygen vacancies tend to be closer to the Gd3+ cation than to the Ce4+ cation with a more pronounced trend for the N arrangement. Deeper analysis of cation coordination is undertaken through the study of the volume, distortion and number of the different coordination polyhedra. The difference observed between the R and the N arrangement could then lead to different physical properties as it is the case for the thermal expansion coefficient previously determined.
Abstract In martensitic transformations, the evolution of elastic fields plays a crucial role in understanding the driving mechanisms of transformation dynamics. This study analyzes the evolution of the local field of elastic strain energy density in conjunction with the transformation microstructure using a multi-field coupled approach. A systematic phase-field simulation is performed to model the martensitic transformation in the ternary alloy CuAlNi. Based on the Gibbs free energy data obtained from microscale analysis, both symmetric and asymmetric free energy potentials are incorporated into the finite-strain phase-field model to explore the fine-tuning of the Landau-based polynomial potential. The effects of modifying the free energy potential on the transformation process are demonstrated through comparative simulations. In the multi-field coupled analysis, two specific types of transformation interfaces, responsible for the majority of interface migration, are coupled with the evolution of two distinct local fields of elastic strain energy density. These fields exhibit significantly higher energy densities than other regions. The detailed coupling mechanisms are explained through the evolution of representative local regions in the phase-field simulation of CuAlNi martensitic transformation, using both symmetric and asymmetric free energy potentials. Notably, the coupling mechanisms are independent of the symmetry of the free energy potential.
Abstract In this study, finite element modeling (FEM) of radial shear rolling (RSR) at 900 °C for a bioresorbable Fe–30Mn–5Si alloy was developed and experimentally validated. The strain rate distribution across the cross-section was nonuniform and strongly dependent on the drawing ratio, with maximal values in the peripheral zones, primarily at the workpiece-roll interface. The stress-strain state was characterized by predominantly compressive stresses in the peripheral zone, transitioning to tensile stresses toward the center, where stress triaxiality reached slightly positive values. Equivalent strain was higher in the peripheral zone than in the central one. FEM-simulated temperature distribution during the RSR revealed significant heating in the peripheral zone (from ∼40 °C to ∼100 °C), which promoted softening processes and minimized the microstructure gradient across the cross-section. Based on these findings, defect-free long-length semi-products with a final diameter of 20 mm were produced via RSR in six passes. Optical microscopy revealed microstructural evolution during RSR, showing formation of an equiaxed, statically recrystallized structure with average grain sizes of 9 ± 2 μ m in the peripheral zone and 15 ± 3 μ m in the central zone under the developed conditions. X-ray diffraction analysis revealed that the RSR produces a two-phase state consisting predominantly of FCC γ -austenite, with trace amounts of HCP cooling-induced ϵ -martensite present in both the peripheral and central zones. The resulting microstructure increased microhardness to ∼275 HV in the peripheral zone and ∼265 HV in the central one. These findings demonstrate the effectiveness of FEM analysis for RSR of the bioresorbable Fe–30Mn–5Si alloy. Thus, the FEM-simulated RSR parameters derived for a chosen strain rate and deformation temperature (the stress-strain state, stress triaxiality, and equivalent strain distributions) enable the production of high-quality, defect-free, long-length semi-products.
Kink bands (KBs) are localized regions that rotate relative to adjacent, undeformed areas. They typically form in crystalline materials characterized by strong structural anisotropy and limited slip activity, such as hexagonal close-packed (HCP) structures and particularly in high- c/a nano-layered HCP crystals such as MAX phases. KBs induces lattice rotation and reorients the structure to activate additional slip systems, aiding in meeting the von Mises criterion. In this work, we propose a new model for KB formation based on the concept of zonal dislocations (ZDs). Continuum-scale thermodynamic analyses demonstrate that this model is both physically plausible and structurally stable. These findings are corroborated by atomic-scale simulations, which confirm the feasibility of ZD formation and reveal its internal structure. This model provides a coherent framework for interpreting plasticity in MAX phases and offers a compelling explanation for the hysteresis often observed during deformation, an open question in their mechanical behavior. Our results show that the formation of a single wall of basal dislocations, followed by their dissociation into ZDs, is significantly more energetically favorable than existing models that rely on the formation of two opposing walls of dislocation dipoles, currently considered the dominant pathway for KB initiation.
