Microstructure features including grain morphology and texture are key factors in determining the properties of laser additively manufactured metallic components. Beyond the traditional trial-and-error approach, which is costly and time-consuming, microstructure control increasingly relies on predictions from mechanistic models. However, existing mechanistic models to predict microstructure and texture are computationally expensive. Here, we present a cellular automata solidification model, which is up to two orders-of-magnitude faster than traditional models. By analytically calculating growth length and utilizing a multi-level capture algorithm, a large time step can be employed without compromising simulation accuracy. The model is validated through simulations of 316L steel and three NiTi cases, showing good agreement with experimental results. Our findings reveal that preferential orientations are selected by the vertical and the inclined temperature gradients from multi-pass temperature profiles, leading to different microstructures and textures. Three-dimensional additive manufacturing simulations demonstrate that orientation-dependent growth patterns govern grain growth, leading to columnar, planar and spiral shaped grains. This approach offers a significant reduction in computational cost while maintaining accuracy, contributing to the practical application of microstructure control in additive manufacturing. The insights gained on grain and texture evolution pave the way for customized microstructure design through additive manufacturing.

A multi-scale multi-physics modeling framework has been developed to predict solidification cracking susceptibility (SCS) during welding. The framework integrates a thermo-mechanical finite element model to simulate temperature and strain rate profiles during welding, a cellular automata model to simulate the solidified microstructure in the weld pool, and a granular model to calculate the pressure drop in the mushy zone. Verification was achieved by comparing the model’s predictions with welding experiments on two steels, demonstrating its capability to accurately capture the effects of process parameters, grain refinement, and alloy composition on SCS. Results indicate that increasing welding velocity, while maintaining a constant power-to-velocity ratio, extends the size of the mushy zone and increases the maximum pressure drop in the mushy zone, leading to higher SCS. Grain refinement decreases separation velocities and the permeability of liquid channels, which increases SCS, but it also raises the coalescence temperature, resulting in an overall reduction in SCS. Alloy composition impacts SCS through thermal diffusivity and segregation. Lower thermal diffusivity or stronger segregation tends to elongate the mushy zone, resulting in an increase in SCS. This framework provides a robust tool for understanding the mechanisms of solidification cracking, optimizing welding parameters to prevent its occurrence, and comparing SCS of different compositions during alloy design.

In the present study, the nucleation of static recrystallization (SRX) in austenite after hot deformation is experimentally analyzed using a Ni-30 pct Fe model alloy. In agreement with the predictions by current models, nucleation rate exhibits a strong peak, early during SRX. Whereas such an early peak is explained by current models by the saturation of nucleation sites, this condition is far from reached, even after the peak declines. In addition, triple-junction and grain-boundary sites are shown to make a quantitatively similar contribution to nucleation. However, for a given boundary between deformed grains, nucleation predominantly starts at one of the triple junctions. Triple-junction nucleation initiates by strain-induced boundary migration of the nucleus (bulging) along one of the boundaries at the junction. Annealing twin boundaries contribute negligibly to nucleation through their grain-boundary sites. By contrast, their junctions with the boundaries of the parent grains do play a relevant role. The earlier nucleation at the triple junctions is attributed to the higher dislocation density observed around them, and the energy of the boundary consumed by the bulge. Both the maximum and average number of nuclei formed per boundary between deformed grains increase with increasing boundary length.

Modelling dislocation glide over the initial part of a stress–strain curve of metals received little attention up to now. However, dislocation glide is essential to ones understanding of the fundamental relationship between inelastic deformation and the evolution of the dislocation network structure. Therefore, we present a model of dislocation-driven deformation under static loading conditions. We reproduce repeated cyclic uniaxial tensile tests on Interstitial-Free and Low-Alloy steels. The elastic mechanical behaviour is described by isotropic linear elasticity, pre-yield anelastic mechanical behaviour by a dislocation bow-out model with dissipation, and the post-yield evolution of dislocation network structure by a statistical storage model. We hypothesise that when the local anelastic compliance is lower than the global plastic compliance, deformation is mechanically recoverable, and vice versa. This hypothesis is corroborated with the classical Taylor relation. We report the relation between stable and unstable dislocation glide using this prototypical modelling framework. We find four structural variables, that are based on dislocation physics, to describe the stress–strain curve: total dislocation density, average dislocation segment length, dislocation junction formation rate, and average dislocation junction length. Firstly, we quantify the dislocation network evolution during uniaxial monotonic loading, and verify work-hardening by dislocation junction formation and a Taylor-type equation for flow. Finally, we present a semi-empirical relation for the evolution of the dislocation network structure. Which allows us to: refine the physical interpretation of the Taylor relationship, and rationalise experimental observations on apparent modulus degradation by thermomechanical processing. Both these findings circumvent the limitations of current, physics-based hardening models.

A cellular automata (CA) model has been developed for solidification simulation considering the kinetic undercooling at the interface. The state-of-the-art model incorporates a decentered growth algorithm to suppress the grid anisotropy and a generalized height function method to calculate the curvature accurately. To develop a CA model which is independent of the mesh size, a new diffusion term is proposed to handle the diffusion between the interface cells and liquid cells. The developed CA model is employed to simulate the single-dendritic solidification of an Al–3Cu (wt pct) alloy. The simulated tip velocities agree with the prediction of the Kurz–Giovanola–Trivedi (KGT) model. Further studies show that the developed CA model converges to an equilibrium model with increasing kinetic mobility values. Moreover, it is found that the virtual liquid cell assumption which is commonly used in existing CA models may lead to a deviation in the mass balance. The mass balance error has been resolved by redistributing solutes from neighboring liquid cells in each time step. The developed CA model could be potentially used in solidification simulations with a high undercooling, which is common in welding and additive manufacturing.

The superconducting critical temperature (T-\mathrm{c} > 15 K) of niobium titanium nitride (NbTiN) thin films allows for low-loss circuits up to 1.1 THz, enabling on-chip spectroscopy and multipixel imaging with advanced detectors. The drive for large-scale detector microchips is demanding NbTiN films with uniform properties over an increasingly larger area. This paper provides an experimental comparison between two reactive dc sputter systems with different target sizes: A small target (o100 mm) and a large target (127 mm × 444.5 mm). This paper focuses on maximizing the T-\mathrm{c} of the films and the accompanying I-V characteristics of the sputter plasma, and we find that both systems are capable of depositing films with T-\mathrm{c} > 15 K. The resulting film uniformity is presented in a second manuscript in this volume. We find that these films are deposited within the transition from metallic to compound sputtering, at the point where target nitridation most strongly depends on nitrogen flow. Key in the deposition optimization is to increase the system's pumping speed and gas flows to counteract the hysteretic effects induced by the target size. Using the I-V characteristics as a guide proves to be an effective way to optimize a reactive sputter system, for it can show whether the optimal deposition regime is hysteresis-free and accessible.

Uniformity in thickness and electronic properties of superconducting niobium titanium nitride (NbTiN) thin films is a critical issue for upscaling superconducting electronics, such as microwave kinetic inductance detectors for submillimeter wave astronomy. In this article we make an experimental comparison between the uniformity of NbTiN thin films produced by two DC magnetron sputtering systems with vastly different target sizes: the Nordiko 2000 equipped with a circular nothing 100 mm target, and the Evatec LLS801 with a rectangular target of 127 mm × 444.5 mm. In addition to the films deposited staticly in both systems, we have also deposited films in the LLS801 while shuttling the substrate in front of the target, with the aim of further enhancing the uniformity. Among these three setups, the LLS801 system with substrate shuttling has yielded the highest uniformity in film thickness (±2%), effective resistivity (decreasing by 5% from center to edge), and superconducting critical temperature (Tc = 15.0 K-15.3 K) over a ∅ 100 mm wafer. However, the shuttling appears to increase the resistivity by almost a factor of 2 compared to static deposition. Surface SEM inspections suggest that the shuttling could have induced a different mode of microstructural film growth.