TY - JOUR A1 - Xing, H. A1 - Jing, H. A1 - Dong, X. A1 - Wang, Lei A1 - Han, Y. A1 - Hu, R. T1 - Cellular growth during rapid directional solidification: Insights from quantitative phase field simulations N2 - In this paper, columnar cellular growth with kinetic effects including kinetic undercooling and solute trapping in rapid directional solidification of alloys was investigated by using a recent quantitative phase-field model for rapid solidification. Morphological transition and primary spacing selection with and without kinetic effects were numerically investigated. Numerical results show that doublon structure is an intermediate state in the primary spacing adjustment of cellular arrays. It was found that the inclusions of kinetic effects result in the increase of the solute in the solid phase and the solute enrichment in the interdendritic liquid channel. Moreover, predicted results indicate that the growth directions of the cellular arrays in rapid directional solidification with and without kinetic effects are independent of the Péclet number. Therefore, the kinetic effects play important roles in numerical simulations of the growth pattern selection and solute distribution during rapid solidification. Neglecting them will result in the inaccurately predicted results. KW - Rapid solidification KW - Phase-field model PY - 2022 DO - https://doi.org/10.1016/j.mtcomm.2022.103170 VL - 30 SP - 103170 PB - Elsevier Ltd. AN - OPUS4-54571 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Ruffini, A. A1 - Le Bouar, Y. A1 - Finel, A. A1 - Epishin, A. I. A1 - Fedelich, Bernard A1 - Feldmann, Titus A1 - Viguier, B. A1 - Poquillon, D. T1 - Dislocations interacting with a pore in an elastically anisotropic single crystal nickel-base superalloy during hot isostatic pressing N2 - The formation of pores in CMSX-4 nickel based superalloys is detrimental to the service life of the material. A way to avoid the problem is to treat the superalloys under Hot Isostatic Pressing (HIP), which enables a large volume fraction of pores to be annihilated. This paper aims to understand the contribution of plastic activity related to the gliding of dislocations on the pore annihilation. Simulations based on a phase-field model of dislocation are performed and make it possible to consider the strong anisotropy of the CMSX-4 under HIP conditions in conjunction to the strong elastic heterogeneity introduced by the pore. For pores with a radius of few micrometers, it is shown that edge parts of dislocation lines that present an extra half atomic plane oriented towards the pore are stacked above and under it in the direction which is perpendicular to their slip-planes, causing an increase of the number of dislocation along the four octahedral directions of the FCC single crystal which intersect the pore center. Results are streamlined within the isotropic elastic theory of dislocations. Effects of elastic anisotropy and dislocation reactions are also investigated in order to specify what would be the dislocation configuration around a pore in CMSX-4 under HIP conditions. Notably, the elastic anisotropy is shown to significantly modify the arrangement of dislocations close to the pore equator. Simulations also allow for the characterization of pore/dislocation interactions when dislocations are involved in Low Angle Boundaries as experimentally observed. KW - HIP KW - Superalloys KW - Dislocation KW - Pore KW - Phase-field PY - 2022 DO - https://doi.org/10.1016/j.commatsci.2021.111118 SN - 0927-0256 VL - 204 SP - 1 EP - 14 PB - Elsevier CY - Amsterdam AN - OPUS4-54220 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Falk, Florian A1 - Sobol, Oded A1 - Stephan-Scherb, Christiane T1 - The impact of the microstructure of Fe-16Cr-0.2C on high-temperature oxidation – sulphidation in SO2 N2 - This study elucidates the impact of the microstructure of Fe-16Cr-0.2C on oxide layer formation at 650 ◦C in Ar-0.5 % SO2. A cold-rolled and two heat-treated states of the alloy were exposed for up to 1000 h. The samples were characterised in detail from microstructural and chemical perspectives using scanning electron microscopy (SEM), X-ray diffraction (XRD) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). The microstructural modification of the alloy by heat-treatment was advantageous. It was found that Cr-carbides support chromia formation and reduce sulphidation when their area fraction is low and diameter is small. KW - Steel KW - Iron KW - SIMS KW - SEM KW - High temperature corrosion KW - Oxidation KW - Sulphidation PY - 2021 DO - https://doi.org/10.1016/j.corsci.2021.109618 VL - 190 SP - 109618 PB - Elsevier Ltd. AN - OPUS4-53001 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mosquera Feijoo, Maria A1 - Oder, Gabriele A1 - Saliwan Neumann, Romeo A1 - Buchheim, Michaela A1 - Kranzmann, Axel A1 - Olbricht, Jürgen T1 - Impact of Sample Geometry and Surface Finish on VM12‑SHC Ferritic–Martensitic Steel Under Cyclic Steam Atmosphere Operating Conditions N2 - The steam side oxidation of ferritic–martensitic VM12-SHC steel was investigated under thermo-cyclic conditions in water steam at 620/320 °C and 30 bar with a focus on assessing the influence of pre-oxidation time, specimen geometry and surface finish. The specimens were pre-oxidized under isothermal conditions in water steam at 620 °C and 30 bar for 500 h or 1500 h. After pre-oxidation treatment, all specimens were subjected up to 258 thermal cycles. Three different geometries—rectangular coupons, U-shaped ring segments and ring samples—were investigated to evaluate the influence of open/closed shape, and flat/curved surface on corrosion rate. At the same time, two types of surface finish were considered: “as received” and “ground.” The formation of a protective scale by pre-oxidation was investigated. EBSD and ESMA analyses revealed that the Cr-content of the alloy appeared to be insufficient for obtaining a protective oxide scale under studied conditions, at the same time the anayses confirmed that initial oxidation depends on presence of minor alloying elements as Si and Mn, strong oxide formers which can alter the kinetics and morphology of the corrosion reaction. Moreover, rectangular coupons with small wall thickness and flat surface exhibited the highest corrosion rate, while “ground” curved samples showed only local oxidation. This indicates that for same pre-oxidation time, oxidation kinetics is controlled by curvature. KW - Cyclic steam oxidation KW - 12%Cr steel KW - Specimen geometry KW - Surface treatments PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-551808 DO - https://doi.org/10.1007/s11085-022-10114-6 SN - 0030-770X SP - 1 EP - 18 PB - Springer AN - OPUS4-55180 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Elfetni, Seif A1 - Darvishi Kamachali, Reza T1 - PINNs-MPF: A Physics-Informed Neural Network framework for Multi-Phase-Field simulation of interface dynamics N2 - We present PINNs-MPF framework, an application of Physics-Informed Neural Networks (PINNs) to handle Multi-Phase-Field (MPF) simulations of microstructure evolution. A combination of optimization techniques within PINNs and in direct relation to MPF method are extended and adapted. The numerical resolution is realized through a multi-variable time-series problem by using fully discrete resolution. Within each interval, space, time, and phases/grains are treated separately, constituting discrete subdomains. PINNs-MPF is equipped with an extended multi-networking (parallelization) concept to subdivide the simulation domain into multiple batches, with each batch associated with an independent NN trained to predict the solution. To ensure continuity across the spatio-temporal-phasic subdomains, a Master NN efficiently is to handle interactions among the multiple networks and facilitates the transfer of learning. A pyramidal training approach is proposed to the PINN community as a dual-impact method: to facilitate the initialization of training when dealing with multiple networks, and to unify the solution through an extended transfer of learning. Furthermore, a comprehensive approach is adopted to specifically focus the attention on the interfacial regions through a dynamic meshing process, significantly simplifying the tuning of hyper-parameters, serving as a key concept for addressing MPF problems using machine learning. We perform a set of systematic simulations that benchmark foundational aspects of MPF simulations, i.e., the curvature-driven dynamics of a diffuse interface, in the presence and absence of an external driving force, and the evolution and equilibrium of a triple junction. The proposed PINNs-MPF framework successfully reproduces benchmark tests with high fidelity and Mean Squared Error (MSE) loss values ranging from 10^−6 to 10^−4 compared to ground truth solutions. KW - Machine learning KW - PINNs KW - Phase-field method KW - Microstructure evolution KW - Parallel training KW - Neural networks PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-629740 DO - https://doi.org/10.1016/j.enganabound.2025.106200 SN - 0955-7997 VL - 176 SP - 1 EP - 22 PB - Elsevier CY - Amsterdam AN - OPUS4-62974 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Elfetni, Seifallah A1 - Darvishi Kamachali, Reza T1 - PINNs-MPF: An Efficient Physics-Informed Machine Learning-based Solver for Multi-Phase-Field Simulations using Tensorflow N2 - This paper introduces PINNs-MPF, a novel Machine Learning-based solver designed for Multi-Phase-Field (MPF) and diffuse interface simulations, offering innovative approaches to address complex challenges in addressing microstructure evolution in polycrystalline materials using Machine Learning. The framework not only surpasses current limitations in handling multi-phase problems but also allows for potential upscaling to tackle more intricate scenarios. Developed in Python, the related code leverages optimized libraries like TensorFlow, showcasing efficiency and potential scalability in materials science and engineering simulations. This framework, integrating advanced techniques such as multi-networking and training optimization, setting a new standard in predictive capabilities and understanding complex physical phenomena. KW - Machine Learning KW - Microstructure Simulation KW - Phase Field PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-631917 DO - https://doi.org/10.1016/j.simpa.2025.100753 SN - 2665-9638 VL - 24 SP - 1 EP - 4 PB - Elsevier B.V. AN - OPUS4-63191 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Zhou, X. A1 - Wei, Y. A1 - Kühbach, M. A1 - Zhao, H. A1 - Vogel, F. A1 - Darvishi Kamachali, Reza A1 - Thompson, G. B. A1 - Raabe, D. A1 - Gault, B. T1 - Revealing in-plane grain boundary composition features through machine learning from atom probe tomography data N2 - Grain boundaries (GBs) are planar lattice defects that govern the properties of many types of polycrystalline materials. Hence, their structures have been investigated in great detail. However, much less is known about their chemical features, owing to the experimental difficulties to probe these features at the atomic length scale inside bulk material specimens. Atom probe tomography (APT) is a tool capable of accomplishing this task, with an ability to quantify chemical characteristics at near-atomic scale. Using APT data sets, we present here a machine-learning-based approach for the automated quantification of chemical features of GBs. We trained a convolutional neural network (CNN) using twenty thousand synthesized images of grain interiors, GBs, or triple junctions. Such a trained CNN automatically detects the locations of GBs from APT data. Those GBs are then subjected to compositional mapping and analysis, including revealing their in-plane chemical decoration patterns. We applied this approach to experimentally obtained APT data sets pertaining to three case studies, namely, Ni-P, Pt-Au, and Al-Zn-Mg-Cu alloys. In the first case, we extracted GB specific segregation features as a function of misorientation and coincidence site lattice character. Secondly, we revealed interfacial excesses and in-plane chemical features that could not have been found by standard compositional analyses. Lastly, we tracked the temporal evolution of chemical decoration from early-stage solute GB segregation in the dilute limit to interfacial phase separation, characterized by the evolution of complex composition patterns. This machine-learning-based approach provides quantitative, unbiased, and automated access to GB chemical analyses, serving as an enabling tool for new discoveries related to interface thermodynamics, kinetics, and the associated chemistry-structure-property relations. KW - Machine learning KW - Digitalization KW - Alloy microstructure PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-543049 DO - https://doi.org/10.1016/j.actamat.2022.117633 SN - 1359-6454 VL - 226 SP - 1 EP - 15 PB - Elsevier CY - Amsterdam AN - OPUS4-54304 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Li, L. A1 - Darvishi Kamachali, Reza A1 - Li, Z. A1 - Zhang, Z. T1 - Grain boundary energy effect on grain boundary segregation in an equiatomic high-entropy alloy N2 - Grain boundary (GB) Segregation has a substantial effect on the microstructure evolution and properties of polycrystalline alloys. The mechanism of nanoscale segregation at the various GBs in multicomponent alloys is of great challenge to reveal and remains elusive so far. To address this issue, we studied the GB segregation in a representative equiatomic FeMnNiCoCr high-entropy alloy (HEA) aged at 450 °C. By combining transmission Kikuchi diffraction, atom probe tomography analysis and a density-based thermodynamics modeling, we uncover the nanoscale segregation behavior at a series of well-characterized GBs of different characters. No segregation occurs at coherent twin boundaries; only slight nanoscale segregation of Ni takes place at the low-angle GBs and vicinal \Sigma 29b coincidence site lattice GBs. Ni and Mn show cosegregation of high levels at the general high-angle GBs with a strong depletion in Fe, Cr, and Co. Our density-based thermodynamic model reveals that the highly negative energy of mixing Ni and Mn is the main driving force for nanoscale cosegregation to the GBs. This is further assisted by the opposite segregation of Ni and Cr atoms with a positive enthalpy of mixing. It is also found that GBs of higher interfacial energy, possessing lower atomic densities (higher disorder and free volume), show higher segregation levels. By clarifying the origins of GB segregations in the FeMnNiCoCr HEA, the current work provides fundamental ideas on nanoscale segregation at crystal defects in multicomponent alloys. KW - Thermodynamics KW - High-Entropy Alloys KW - Grain Boundary Segregation PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-508827 DO - https://doi.org/10.1103/PhysRevMaterials.4.053603 VL - 4 IS - 5 SP - 053603 PB - American Physical Society AN - OPUS4-50882 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Röhsler, Andreas A1 - Sobol, Oded A1 - Hänninen, H. A1 - Böllinghaus, Thomas T1 - In-situ ToF-SIMS analyses of deuterium re-distribution in austenitic steel AISI 304L under mechanical load N2 - Hydrocarbons fuel our economy. Furthermore, intermediate goods and consumer products are often hydrocarbon-based. Beside all the progress they made possible, hydrogen-containing substances can have severe detrimental effects on materials exposed to them. Hydrogen-assisted failure of iron alloys has been recognised more than a century ago. The present study aims to providing further insight into the degradation of the austenitic stainless steel AISI 304L (EN 1.4307) exposed to hydrogen. To this end, samples were electrochemically charged with the hydrogen isotope deuterium (2H, D) and analysed by scanning electron microscopy (SEM), electron back-scatter diffraction (EBSD) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). It was found that deuterium caused a phase transformation from the original γ austenite into ε- and α’-martensite. Despite their low solubility for hydrogen, viz. deuterium, the newly formed phases showed high deuterium concentration which was attributed to the increased density of traps. Information about the behaviour of deuterium in the material subjected to external mechanical load was gathered. A four-point-bending device was developed for this purpose. This allowed to analyse in-situ pre-charged samples in the ToF-SIMS during the application of external mechanical load. The results indicate a movement of deuterium towards the regions of highest stress. KW - ToF-SIMS KW - Hydrogen KW - Deuterium KW - AISI 304L KW - EBSD PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-505142 DO - https://doi.org/10.1038/s41598-020-60370-2 VL - 10 IS - 1 SP - 3611 PB - Nature AN - OPUS4-50514 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kindrachuk, Vitaliy A1 - Darvishi Kamachali, Reza T1 - Mean-field modeling and phase-field simulation of grain growth under directional driving forces N2 - Directional grain growth is a common phenomenon in the synthetic and natural evolution of various polycrystals. It occurs in the presence of an external driving force, such as a temperature gradient, along which grains show a preferred, yet competitive, growth. Novel additive manufacturing processes, with intense, localized energy deposition, are prominent examples of when directional grain growth can occur, beneath the melting pool. In this work, we derive a phenomenological mean-field model and perform 3D phase-field simulations to investigate the directional grain growth and its underlying physical mechanisms. The effect of the intensity of driving force is simulated and systematically analyzed at the evolving growth front as well as various cross-sections perpendicular to the direction of the driving force. We found that although the directional growth significantly deviates from normal grain growth, it is still governed by a power law relation α tⁿ with an exponent n ~ 0.6–0.7. The exponent n exhibits a nontrivial dependence on the magnitude of the directional driving force, such that the lowest growth exponent is observed for intermediate driving forces. We elaborate that this can originate from the fact that the forces at grain boundary junctions evolve out of balance under the influence of the directional driving force. With increasing the driving forces, the growth exponent asymptotically approaches a value of n ≈ 0.63, imposed by the largest possible grain aspect ratio for given grain boundary energies. The current combined mean-field and phase-field framework pave the way for future exploration in broader contexts such as the evolution of complex additively manufactured microstructures. KW - Additive manufacturing KW - Phase-field simulation KW - Grain growth KW - Mean-field modelling KW - Directional grain growth PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-593210 DO - https://doi.org/10.1016/j.mtla.2023.101989 SN - 2589-1529 VL - 33 SP - 1 EP - 10 PB - Elsevier AN - OPUS4-59321 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -