Papers by Keyword: Recrystallization

Abstract: In bulk SiC crystal growth using the PVT method, recrystallization within the source material leads to a decrease in growth rate and source utilization. In this study, numerical simulations were used to investigate the source temperature distribution and its effect on the growth rate and source utilization. Recrystallization in the upper and lower regions was considered separately. The results showed that reducing the source temperature gradient prevents recrystallization in the upper region, and a unidirectional gradient prevents recrystallization in the lower region, leading to higher growth rates and source utilization.

Abstract: A new efficient numerical solver inspired by front-tracking concepts is implemented within the DIGIMU® framework to accelerate full-field simulations of microstructural evolution. The solver is applied to AISI 304L stainless steel and compared with the conventional level-set formulation under laboratory hot-torsion tests and industrial multi-pass hot rolling conditions. After a limited recalibration of grain boundary mobility and solute drag parameters, both solvers provide comparable predictions of recrystallization kinetics, grain size evolution and final microstructures. The new solver achieves a reduction in computational cost close to two orders of magnitude, while preserving the predictive capabilities of DIGIMU®, thereby enabling more efficient industrial-scale simulations. Simulated predictions will be compared to Ugitech experimental work on lab torsion tests and industrial extrusion processes.

Abstract: Predicting the microstructural state during manufacturing is critical, as it directly governs the material's final mechanical properties. Accurate prediction of microstructure evolution in multi-stage industrial hot deformation processes, such as rolling, is limited by the lack of experimental data at intermediate stages, where direct measurement is impractical. To address this, an integrated methodology combining finite element (FE) simulation in QForm UK® software, physical simulation using the Thermo-Mechanical Treatment Simulator (TMTS), and artificial intelligence (AI) is proposed and investigated. The methodology is demonstrated for the 11-pass hot rolling of a 41Cr4 steel bar. Thermomechanical loading histories from an FE model of the industrial process were used to design and simulate a targeted TMTS experiment, generating a synthetic dataset via an analytical JMAK model that combines multiple recrystallisation mechanisms. This data was used to train a recurrent neural network (RNN) with an augmented physics-informed Long Short-Term Memory (LSTM) cell to predict the totally recrystallised fraction (RX) solely from loading history data. The AI model achieved high accuracy when validated within the TMTS simulation domain, successfully capturing different recrystallisation regimes. Implementation within commercial FE software enabled direct prediction in the rolling process simulation, yielding promising predictive capability, particularly in regions with thermal histories similar to the training data, highlighting the critical importance of training data diversity. This work establishes a proof of concept for a novel calibration methodology, where targeted physical simulation bridges the gap between industrial process complexity and data-driven AI model development, offering a practical solution for modelling scenarios where traditional experimental calibration is infeasible.

Abstract: In this study, we develop a Bayesian data assimilation framework that combines a mean-field model of static recrystallization (MiReX) with a Sequential Importance Resampling (SIR) particle filter to estimate key material parameters from controlled synthetic experiments. MiReX, originally developed as a microstructurally based extension of Johnson–Mehl–Avrami–Kolmogorov kinetics, is used as a forward model in which the uncertain quantities include the grain-boundary mobility parameters (prefactor and activation energy), a stored-energy coefficient, an Avrami-type exponent, and an interface length scale. Synthetic recrystallized-fraction measurements are generated at two isothermal holding temperatures using a reference parameter set and are perturbed with Gaussian noise to mimic experimental uncertainty. Starting from broad uniform prior ranges, the particle filter propagates an ensemble of MiReX trajectories in time, updates particle weights using a Gaussian likelihood, and applies systematic resampling combined with Liu–West kernel regularization to reduce particle degeneracy while preserving posterior variance. The posterior obtained after assimilating the first temperature dataset is used as the prior for the second dataset, enabling sequential multi-temperature calibration. The synthetic experiments show that the framework recovers the reference parameters within credible intervals and provides tight uncertainty bounds on the predicted recrystallization kinetics. These results demonstrate that combining a physically based mean-field recrystallization model with sequential Monte Carlo methods provides a robust route for probabilistic parameter estimation and uncertainty quantification in microstructure evolution models.

Abstract: This paper presents experimental results on the processing of complex concentrated alloy with a nominal composition of A_0.35CoCrFeNi. The alloy was produced by vacuum induction melting and tilt casting. The microstructure of the as-cast CCA consists of dendritic and interdendritic regions homogenized by heat treatment at 1360 °C. After rotary swaging at room temperature, the microstructure is characterized by an abundance of dislocations and continuously intersecting slip bands. Annealing experiments were carried out in the temperature range of 1150 °C – 1300 °C and different holding times to determine the parameters of grain growth kinetics. Phase and chemical analysis were investigated using XRD and EDS methods. The activation energy of recrystallization in the studied composition was 458 kJ mol^-1. The influence of grain size on room temperature mechanical properties and tensile properties was determined. The hardening coefficients k_h and k_σ, calculated using the Hall-Petch relation, were 277.5 HV µm^-1/2 and 655 MPa µm^-1/2, indicating the effectiveness of grain boundary hardening in the studied single-phase CCA.

Abstract: The formability of Aluminum 2050 alloy is critical for manufacturing large and thick components while maintaining its outstanding performance. To link the damage development during high-temperature loading with the alloy microstructure evolution, a time resolved tensile loading experiment at 480 °C was performed on this alloy using synchrotron diffraction and tomography, i.e. diffraction contrast tomography (DCT) to provide 3D grain maps and phase contrast tomography (PCT) to characterize pores and intermetallics. The evolution of both was quantified as a function of macroscopic strain up to 20.15%. Three pore formation mechanisms were identified: growth from pre-existing pores, fracture of the intermetallics, and nucleation of new pores. The characteristics of the pore evolution are linked with the grain structure characterized by DCT. Additionally, the grain maps reconstructed for initial and final strained states show newly recrystallized grains, indicating the presence of dynamic recrystallization. To exclude the possible explanation by annealing recrystallization, an extra annealing experiment was performed and no recrystallized grains were observed. A comprehensive insight into linking the damage development with the microstructure evolution under high-temperature deformation has been obtained by using synchrotron grain mapping techniques and tomography.

Abstract: Microstructural evolution during D.C. casting and subsequent homogenization of non-heat-treatable aluminium alloys involves complex phenomena, including micro-segregation of alloying elements and intermetallic phase selection during solidification as well as phase transformations of both primary (constituents - intergranular) and secondary (dispersoids - intragranular) intermetallic phases. In this study, we simulated the microstructural evolution of AA3003 using a CALPHAD-based modelling framework implemented in ThermoCalc®. The framework integrates a Scheil-Gulliver solidification model coupled with a 1-D micro-segregation alleviation and diffusional phase transformation model (DICTRA®) and a Kampmann-Wagner Numerical (KWN) model for dispersoid precipitation (TC-PRISMA®). According to this approach, the development of a robust computational methodology is aimed at accurately predicting the influence of homogenization cycles on dispersoid precipitation, which in turn affects recrystallization behaviour via the well-known Smith-Zener drag phenomenon. Additionally, these CALPHAD-based simulations facilitate the assessment of impurity content effects on dispersoid precipitation, considering the increasing use of scrap in the fabrication of non-heat-treatable aluminium alloys. Furthermore, they provide precise estimates of Smith-Zener pinning forces as inputs for downstream mesoscale full-field process models, contributing to a holistic through-process modelling approach.

Abstract: In the present work artificial neural networks (ANN) models have been implemented and trained as surrogate models to replicate two physics-based microstructure models for Al-alloys, i.e. the ALFLOW model, which predicts the sub-structure evolution and associated flow stress during plastic deformation and the softening model ALSOFT, which predicts the softening behavior after hot/cold deformation, in view of the combined effect of recovery and recrystallization. Input for both ANN models was limited to variables such as strain, strain rate, time, temperature and solute concentration, and the flow stress as the output. Accuracy and efficiency were tested for different ANN architectures. It is demonstrated that fully connected feed-forward neural network architectures with ∼3 hidden layers are suitable as surrogate models for both ALFLOW and ALSOFT, with a potential speed-up of ∼100x for ALFLOW and ∼10x for ALSOFT.

Abstract: The effects of silicon (Si) addition and continuous annealing (CA) parameters on the microstructure and mechanical properties of low carbon Nb-Ti steels were investigated. Steels with and without Si were subjected to CA simulations, varying annealing temperature, line speed (LS), and cold work (CW) levels. Low-temperature thermomechanical controlled processing (TMCP) during hot rolling produced a fine polygonal ferrite matrix with uniformly distributed, spherical cementite - finer and more homogeneously dispersed in the Si-containing steel. Surface oxides in the as-rolled Si steel consisted mainly of wüstite and magnetite, with no deleterious hematite or fayalite observed due to high temperature descaling. Recrystallization during CA began near 650°C and completed above 780°C but was delayed by Si addition, higher line speeds, and moderate cold work. The final ferrite grain size remained fine, averaging 4–5 μm, across a broad annealing temperature range, aided by effective grain boundary pinning from carbonitrides. In the 690–760°C annealing range, the Si-containing steel exhibited increased strength due to solid solution strengthening, carbonitride precipitation hardening and restricted recrystallisation. Despite this, elongation was preserved through the formation of fine, soft, ductile, uniformly dispersed spherical cementite (Fe₃C) in the Si steel. Higher levels of cold work reduced strength slightly after annealing above 780°C but improved elongation due to full recrystallisation and coarsening of NbTi (C,N) particles.

Abstract: The Ti-13Nb-1.5Mo-3Ta alloy is a recently developed biocompatible metastable β-Ti alloy designed for biomedical application. In this present work, the influence of cold rolling and subsequent annealing heat treatment on grain refinement of Ti-13Nb-1.5Mo-3Ta alloy was investigated. The alloy was cold rolled (CR) to 60% and 90% thickness reductions at room temperature followed by recrystallization annealing at different temperature (800°C-900°C) and time (1.5mins-10mins) before ice-water quenching. X-ray diffraction (XRD) and optical microscopy (OM) were used to characterize the alloy, and microhardness tests were carried out using the Vickers microhardness tester. The results revealed that the annealed alloys exhibited a fully β-phase, while those subjected to cold rolling displayed introduction of stress induced martensite (SIM) α′′-phase along with β-phase. The microhardness of the 60% and 90%CR samples increased significantly to 253 and 283 Vickers hardness (HV), respectively, from an initial value of 198 HV. Annealed samples exhibited a recrystallized microstructure containing fine equiaxed grains with average size of 10-50μm for 60%CR and 8-34μm for 90%CR. The grain refinement mechanisms are probably attributed to the reversal of the SIM α′′-phase back to the more stable β-phase and the recrystallization of the deformed β-phase.