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Recently, there has been development toward metal additive manufacturing (MAM) because of its benefits like fabrication of complex geometries, waste minimization, freedom of design, and low-cost customization. Despite these advantages, the influence of the processing parameters on the properties of MAM products is neither well understood nor easily predictable. In this study, explainable machine learning (xML) models were applied to predict and understand the geometry and types of defects in MAM-processed Fe-Ni alloys. Gaussian process regression (GPR) was used to predict the as-printed height and porosity using data from Fe-Ni alloys produced via laser metal deposition (LMD) processing. Defect types (gas porosity, keyhole, and lack of fusion) were classified using a support vector machine (SVM) by comparing the measured and predicted porosities based on GPR. The Shapley additive explanation (SHAP) approach for xML was utilized to analyze feature importance based on both GPR and SVM data. This study provides insight into the use of the xML model in MAM to link processing with results.

Abstract The digitized format of microstructures, or digital microstructures, plays a crucial role in modern-day materials research. Unfortunately, the acquisition of digital microstructures through experimental means can be unsuccessful in delivering sufficient resolution that is necessary to capture all relevant geometric features of the microstructures. The resolution-sensitive microstructural features overlooked due to insufficient resolution may limit one’s ability to conduct a thorough microstructure characterization and material behavior analysis such as mechanical analysis based on numerical modeling. Here, a highly efficient super-resolution imaging based on deep learning is developed using a deep super-resolution residual network to super-resolved low-resolution (LR) microstructure data for microstructure characterization and finite element (FE) mechanical analysis. Microstructure characterization and FE model based mechanical analysis using the super-resolved microstructure data not only proved to be as accurate as those based on high-resolution (HR) data but also provided insights on local microstructural features such as grain boundary normal and local stress distribution, which can be only partially considered or entirely disregarded in LR data-based analysis.

Due to their low coefficient of thermal expansion, Invar sheets are used as the material for fine metal masks (FMM). In producing theses masks, the sheets are often chemically etched to produce arrays of micro-holes. Because etching behavior of a material depends not only on the chemical composition of the material, but also on the surface microstructure of the material, an in depth exploration on the role of the surface microstructure of an Invar alloy on the etching behavior of the alloy is fundamental in manufacturing high quality FMMs. In this study, microstructure dependent etching behavior of a partially recrystallized Invar sheet is investigated. The Invar alloy was partially recrystallized at 750℃ to examine how recrystallized and non-crystallized grains differ in their etching behaviors using electron backscatter diffraction (EBSD) and atomic force microscope (AFM) techniques. The results show that the overall etching behavior of the partially recrystallized Invar sheet not only depends on the properties of individual grains such as orientation dependent surface energy and geometrically necessary dislocation (GND) content, but also on the properties of aggregates of grains such as grain boundary density.

Data-driven approaches enable a deep understanding of microstructure and mechanical properties of materials and greatly promote one's capability in designing new advanced materials. Deep learning-based image processing outperforms conventional image processing techniques with unsupervised learning. This study employs a variational autoencoder (VAE) to generate a continuous microstructure space based on synthetic microstructural images. The structure-property relationships are explored using a computational approach with microstructure quantification, dimensionality reduction, and finite element method (FEM) simulations. The FEM of representative volume element (RVE) with a microstructure-based constitutive model model is proposed for predicting the overall stress-strain behavior of the investigated dual-phase steels. Then, Gaussian process regression (GPR) is used to make connections between the latent space point and the ferrite grain size as inputs and mechanical properties as outputs. The GPR with VAE successfully predicts the newly generated microstructures with target mechanical properties with high accuracy. This work demonstrates that a variety of microstructures can be candidates for designing the optimal material with target properties in a continuous manner.

Invar alloy possesses a uniquely low coefficient of thermal expansion, making it an ideal material for fine metal masks. To manufacture fine metal masks, Invar alloys are often cold-rolled, during which residual stress develops. Heat treatment is an effective means to control residual stress that develops within Invar sheets after cold rolling, but the treatment should be carried out with care. In this article, a comprehensive study on the effect of heat treatment on the residual stress, microstructure, and mechanical properties of a cold-rolled Invar sheet is reported. We show that while both recovery and recrystallization are effective means of reducing residual stress, substantial microstructural changes and, therefore, notable changes in mechanical properties and residual stress, occur after recrystallization. Moreover, residual stress release due to recrystallization can be affected by microstructure and texture prior to heat treatment as these factors play a significant role in recrystallization.

The high-temperature deformation behavior of metals and alloys undergoes complex mechanisms depending on the deformation conditions. The microstructure and mechanical properties after deformation are important factors that determine the strength and durability of the final product. Therefore, many studies to predict the microstructure and mechanical properties have been conducted. In this regard, numerous mathematical approaches for predicting microstructure and flow stress have been proposed over the past half century. Accordingly, many advances have been made in the field of material science. Nevertheless, there are limitations in the mathematical modeling method as there is a complex relationship between the deformation conditions and the mechanical properties. Therefore, in this study, flow stress prediction was performed by applying conventional constitutive equation and artificial intelligence technology, which is known to be effective in modeling complex relationships. As a result, it was confirmed that the flow stresses modeled by the artificial neural network showed a higher accuracy than the flow stresses modeled by the conventional Arrhenius hyperbolic sine equation.

In this paper, the extrusion process of titanium seamless tubes was studied using several finite element (FE) analyses. First, the finite element result was compared with experimental extrusion data acquired to validate the current analysis. Then, the effect of design parameters of the die shape was numerically analyzed using commercial FE software, Forge NxT, for the metal forming process. Elastic FE analyses were also conducted for dies to analyze the maximum principal stress that affects the early fracture of dies during the extrusion process and the maximum von Mises stress that causes the severe deformation of dies. Consequently, the effect of the corner radius at the exit and land length on the extrusion load and die stress is negligible compared to that of the corner radius at the entrance and die angle. Finally, we suggested a die angle of 60° and a corner radius at the die entrance between 10 and 15 mm as an optimal design for the current extrusion process.

Metals: grain neighbours influence twin formation during deformation Grains that should not favour twin formation exhibit twinning as a result of surrounding grains acting on their boundaries. A team led by HyoungSeop Kim at the Pohang University of Science and Technology in the Republic of Korea simulated the deformation of synthetic metallic microstructures with many grains of different orientations, based on steels that deform by both dislocation slip and twinning mechanisms. Twinning first started near grain boundaries and depended on initial grain orientation but, with further deformation, strong twin activity on one side of a boundary triggered strong twin activity on the other side of that boundary. This happened even when the grain on the other side of the boundary was unfavourable to twinning. Taking into account grain neighbourhood may therefore help in optimising twin-forming alloys.

In molten carbonate fuel cell (MCFC) systems, it is known that the shape of corrugated plates has a significant influence on performance, durability, and cost. A corrugated plate with a repeating open trapezoidal-shaped slot supports membrane electrode assembly and provides a gas flow channel. To increase the efficiency of the MCFC, the slot between the corrugated and center plates has a relatively large contact length. However, increasing the contact length of the slot increases the risk of necking or fracture generation at the corner of the slot. Therefore, we focus on the development of forming technology of corrugated plate which has large contact length of slots without any necking or fracture. To this end, numerical simulation was conducted to determine the appropriate process and tool design. In the simulation, to capture shear fracture during the forming process of slots, the normalized Cockroft–Latham ductile fracture model was used. The critical value for slitting and fracture was evaluated by comparing the deformed shapes in the slitting plane obtained from experimental and simulation results. Based on simulation results, a reasonable design concept of the two-stage forming process was suggested to increase the contact length of the slot without necking or fracture. In addition, the experiment results confirmed the validity of the proposed forming process and tool design.

We have investigated the effect of initial microstructures on the change in critical current density (Jc) of Nb-46.5 wt%Ti (NbTi) superconducting material. It is well known that α-Ti phases distributed in NbTi material act as a flux pinning center, resulting in an improvement in critical current density. Therefore, it is crucial to obtain the grain-refined microstructure, which is strongly related with precipitation of uniformly distributed fine α-Ti phases and higher volume faction of α-Ti phases, as α-Ti phases are precipitated at the grain boundaries and triple points during heat treatments. Therefore, in order to characterize the effect of initial microstructure of NbTi on critical current density, different initial microstructures were obtained by applying equal channel angular pressing (ECAP) and hot rolling with different strains. It was revealed experimentally that hot rolling with a higher strain is efficient for obtaining the initial microstructure, which has equiaxed fine grains of β-NbTi with the aid of dynamic recrystallization, and which is helpful for precipitating fine α-Ti phases during intermediate heat treatment. Furthermore, it was confirmed that critical current density can be enhanced by obtaining a smaller α-Ti phase, a higher aspect ratio of α-Ti phase, a higher volume fraction of α-Ti phase and a ribbon-like folded α-Ti phase.