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A new type of gradient structure is fabricated by the sequential processing of two advanced severe plastic deformation methods, single-roll angular-rolling and ultrasonic nanocrystalline surface modification, which refine microstructures mainly on the core and surface of a metallic sheet, respectively. This material with a multi-layered grain size gradient in the thickness direction features impressive strength-toughness synergy beyond the initial and reverse gradient-structured materials. The superiority is attributable to hetero-deformation induced mechanisms that originate from microstructural heterogeneity. Ultimately, this work suggests a new processing approach by which a strength-toughness window saturated at a certain level in gradient-structured materials can be extended.

Constitutive modeling of CoCrFeMnNi high-entropy alloy (HEA) at cryogenic temperature (77 K) and room temperature (293 K) has been investigated. The effect of temperature on deformation behavior such as twinning, forest hardening, and back stress hardening has been established. The enhanced ductility and strength of CoCrFeMnNi HEA at 77 K are due the combination of sub-grain structure, twinning, and dislocations. This phenomenon is explained in terms of quantitative values of twin volume fraction, inter-twin spacing, and dislocation density. The isotropic kinematic constitutive model is constructed with a critical twinning stress parameter to obtain the criteria for twinning initiation. The developed finite element model simulation results at 77 K and 293 K are in good agreement with the experimental data. The model displays a smooth increase in the twin volume fraction until fracture point (maximum twin fraction region). Also, different modeling parameters are obtained for each temperature to account for the changing deformation behavior.

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.

In this research, we investigate threshold voltage drift in a B-Te based OTS device under various operation conditions. To this aim, drift of threshold voltage after a switching process is examined. From the measurements, we find that the threshold voltage required to switch the device back after the first switching process decreases when the interval between the voltage pulsespplied for the switching is shorter than $10 \mu \text{s}$ , and becomes drift-free afterward. This result implies that a certain amount of heat is generated during the first switch which results in higher local temperature that lowers the threshold voltage, and is dissipated completely after $10 \mu \text{s}$ . A 3D finite element simulation is provided to demonstrate the thermal effect induced by the switching pulse. Our results demonstrate that the threshold voltage of the Te-based OTS device is affected by thermal effects during switching at short time scales, and thus indicate that the threshold voltage drift should be considered carefully in the fast device operation.

We present a high-performance flexible triboelectric nanogenerator (TENG) based on an interlocked array of surface-functionalized indium tin oxide (ITO) nanohelix (NH) structures. The structural pr...

This paper reports the mechanical properties and formability under biaxial stretching in pure tantalum as a function of average grain size. The grain size of pure tantalum was adjusted from submicron to tens of micron using the high-pressure torsion process and subsequent annealing. The stretch formability was evaluated using a miniaturized Erichsen tester. Under uniaxial tension, the mechanical properties of pure tantalum followed the typical strength-ductility trade-off behavior according to the average grain size. Nevertheless, the stretch formability became rather significantly inferior in the coarse-grain tantalum, which was primarily attributed to the poor resistance to strain localization and limited work hardening capacity. This deterioration was supposed to be due to the intensified strain localization with increasing the average grain size, based on the in-grain deformation heterogeneity within individual grains and the surface roughening after the Erichsen test. Consequently, this study suggests that the excellent stretch formability and work hardening capacity under biaxial loading can be achieved at a certain range of the average grain size (8.25–19.3 μm in this work).

To clarify the direction of microstructure design for improving stretch-flangeability, relationships of stretch-flangeability to microstructure and mechanical properties of ultra-high-strength dual-phase (DP) steels were investigated. Microstructure of relatively simple ferrite-martensite DP steels was modified by intercritical annealing, then the effects of microstructure modification on stretch-flangeability, tensile properties, and fracture resistance of the DP steels were systematically quantified. The hole-expansion ratio (HER) increased linearly with an increase the apparent fracture initiation energy, but was not significantly correlated with any individual microstructural properties of DP steels, which have been reported to correlate with HER (e.g., the fraction of martensite, the carbon content of martensite, or the hardness difference between ferrite and martensite). To increase the stretch-flangeability of an ultra-high-strength DP steels, its microstructure should be designed to increase its fracture toughness (i.e., microstructure with low mechanical heterogeneity).

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.

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.

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.

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.

A convolutional neural network (CNN), which exhibits excellent performance in solving image-based problem, has been widely applied to various industrial problems. In general, the CNN model was applied to defect inspection on the surface of raw materials or final products, and its accuracy also showed better performance compared to human inspection. However, surfaces with heterogeneous and complex backgrounds have difficulties in separating defects region from the background, which is a typical challenge in this field. In this study, the CNN model was applied to detect surface defects on a hierarchical patterned surface, one of the representative complex background surfaces. In order to optimize the CNN structure, the change in inspection performance was analyzed according to the number of layers and kernel size of the model using evaluation metrics. In addition, the change of the CNN’s decision criteria according to the change of the model structure was analyzed using a class activation map (CAM) technique, which can highlight the most important region recognized by the CNN in performing classification. As a result, we were able to accurately understand the classification manner of the CNN for the hierarchical pattern surface, and an accuracy of 93.7% was achieved using the optimized model.

The cornerstone of materials design is solving materials-related optimization problems to obtain microstructural or processing variables that lead to the most desirable material properties. Because the objective of materials design is to maximize their performance, the related optimization problems often require a global solution. This type of unconstrained optimization overlooks the feasibility of the solution, which is a key engineering issue. For any practical application, feasibility should be reflected in the constraints included in the optimization problems. Nevertheless, the constraints related to feasibility are considerably complex due to the high dimensionality of the design space and non-physical aspects of the constraints, such as machine specifications, material dimensions, and available initial microstructure. In this work, we propose the use of a simple support vector machine (SVM) trained with information in an existing database to model complex feasibility constraints for material optimization. We present a problem involving optimization of the initial texture of a body-centered cubic (BCC) polycrystalline material to obtain specific target textures after cold-rolling. Both unconstrained and constrained optimizations are conducted for comparison, and the results demonstrate that constrained optimizations yield viable solutions while unconstrained optimizations do not.

An essential task in materials science and engineering is in quantifying the linkages between physical variables of a material to its properties. These linkages are both complex and computationally expensive to quantify, as evidenced by rigorous modeling efforts and time-consuming simulations. Hence, practicality dictates that tasks such as materials design that require numerous evaluations are largely limited to qualitative assessment or traditional trial and error. In this work, microstructure-based simulations with model parameters calibrated to reproduce experimental data are employed to make a qualitative assessment of how physical variables of dual-phase steel are correlated to its properties. Afterward, the linkages between physical variables of dual phase steel to its property are computed with a limited amount of microstructure-based simulation data by adopting the Bayesian approach, namely Gaussian process regression (GPR). Even with a small amount of data, GPR yielded an impressive level of accuracy. Furthermore, because microstructure-based simulations are based on experimental data, the quantified linkages are transferable to experimental data.

Full-field simulations with synthetic microstructure offer unique opportunities in predicting and understanding the linkage between microstructural variables and properties of a material prior to or in conjunction with experimental efforts. Nevertheless, the computational cost restrains the application of full-field simulations in optimizing materials microstructures or in establishing comprehensive structure-property linkages. To address this issue, we propose the use of machine learning technique, namely Gaussian process regression, with a small number of full-field simulation results to construct structure-property linkages that are accurate over a wide range of microstructures. Furthermore, we demonstrate that with the implementation of expected improvement algorithm, microstructures that exhibit most desirable properties can be identified using even smaller number of full-field simulations.

The effect of grain size on stretch-flangeability was investigated to determine its influence on stretch-flangeability of high strength steels. To avoid other effects of microstructure, single-phase twinning-induced plasticity (TWIP) steels were selected for the investigation. To control the grain size of two types of TWIP steels, 1) the initial specimen was annealed at 1100 ℃ to increase its grain size, or 2) subjected to high-pressure torsion then annealed at 650 ℃ to reduce the grain size. The microstructural features were analyzed using the electron backscatter diffraction. The stretch-flangeability of TWIP steels with various grain sizes was evaluated using a hole-expansion test. It was found that the hole-expansion ratio follows the Hall-Petch correlation as does fracture toughness. To improve the stretch-flangeability of high strength steels, microstructural features should be designed to increase their fracture toughness.

In the present study, the effect of secondary phase particles and deformation localization on tensile behaviors of Mg-Zn-Ca alloy were investigated using micro-digital image correlation method. To isolate the effect of the secondary phase particles on tensile behavior, the fractions of the secondary phase particles were adjusted by controlling the annealing temperature. Thermodynamic calculations were performed to identify the fractions of the secondary phase particles. The finite element method was conducted to confirm the localized deformation around the secondary phase particles considering 3-dimensional microstructure. The results suggest that the total fraction of the secondary phase particles affected the localized deformation around the particles and micro-crack propagation process during a tensile test.

A new methodology based on the strain energy release maximization (SERM) theory and Avrami-type kinetics is introduced to predict the evolution of recrystallization texture in a non-grain oriented (NGO) electrical steel. The deformation orientation and the activated slip system of each orientation, which can be developed by cold rolling for a hot-rolled NGO electrical steel, were calculated using the finite element method and visco-plastic self-consistent model. Afterwards, the recrystallization orientations that can evolve from each deformation orientation were determined by the SERM theory, and their fraction over the annealing time was calculated based on the Avrami-type kinetic equation. As a result, this approach for the NGO electrical steel could successfully predict the formation of γ-fiber with strong {1 1 1}〈1 1 2〉 component during recrystallization, which was in good agreement with the experimental results.

In this work, the mechanical characteristics and microstructural evolution of Al0.5CoCrFeMnNi high-entropy alloy (HEA) were studied after annealing at various temperatures (1000, 1100, and 1200 °C). X-ray diffraction, scanning electron microscopy, and energy dispersive spectroscopy analyses were performed to reveal the phase and microstructural variations. The mechanical properties related to different microstructures of the alloy were characterized using tensile testing with digital image correlation. Annealing at lower temperatures led to a higher fraction of B2 phase and finer grain size of FCC (face-centered cubic) phase. A good combination of strength and ductility in this alloy was attributed to the ductile FCC matrix and hard secondary B2 phase. The alloy showed the active evolution of deformation twinning due to the low stacking fault energy when Al was added to CoCrFeMnNi to make the HEA. However, for alloy annealed at lower temperatures, twinning activity was suppressed by the smaller size of grains and depletion of Al content in the FCC matrix. The correlation between the microstructure and mechanical properties was also explored using a simple composite model.

We propose a system for evaluating the stretch-flangeability of small-scale specimens based on the hole-expansion ratio (HER). The system has no size effect and shows excellent reproducibility, reliability, and economic efficiency. To verify the reliability and reproducibility of the proposed hole-expansion testing (HET) method, the deformation behavior of the conventional standard stretch-flangeability evaluation method was compared with the proposed method using finite-element method simulations. The distribution of shearing defects in the hole-edge region of the specimen, which has a significant influence on the HER, was investigated using scanning electron microscopy. The stretch-flangeability of several kinds of advanced high-strength steel determined using the conventional standard method was compared with that using the proposed small-scale HET method. It was verified that the deformation behavior, morphology and distribution of shearing defects, and stretch-flangeability results for the specimens were the same for the conventional standard method and the proposed small-scale stretch-flangeability evaluation system.

Herein, the deep drawability and deep drawing behavior of an equiatomic CoCrFeMnNi HEA and its microstructure and texture evolution are first studied for future applications. The CoCrFeMnNi HEA is successfully drawn to a limit drawing ratio (LDR) of 2.14, while the planar anisotropy of the drawn cup specimen is negligible. The moderate combination of strain hardening exponent and strain rate sensitivity and the formation of deformation twins in the edge region play important roles in successful deep drawing. In the meanwhile, the texture evolution of CoCrFeMnNi HEA has similarities with conventional fcc metals.

Particles or precipitates in physical metallurgy are important microstructural components which control mechanical properties and phase transformation behaviors such as the recrystallization, grain growth and anisotropy of metallic materials. In particular, particle stimulated nucleation followed by an oriented growth of P-oriented grains is a commonly accepted premise, and many industrial grade cold rolled Al alloys exhibit strong P-texture after annealing. Accordingly, substantial efforts have been invested in texture and engineering studies to explore exactly how particle stimulated nucleation leads to the formation of P-oriented grains after annealing in cold-rolled Al alloys. Despite extensive experimental observations on the formation of P-oriented grains, the theoretical grounds for the formation of P-oriented grains is not yet fully established. In this work, we employ crystal plasticity theory and strain energy release maximization theory to uncover the nucleation mechanism of P-orientations near a coarse precipitate of a plane strain compressed Al alloy. The strain energy release maximization theory used in this work demonstrates that the nucleation of P-orientation is primarily possible due to stable P-orientations and near P-orientations formed during plane strain compression, which act as nuclei that recrystallize into P-orientations.

Stretch-flangeability evaluated using hole-expansion testing represents the ability of sheet materials to resist edge fracture during complex shape forming. Despite a property imperative for automotive part applications of advanced high-strength steels, factors governing stretch-flangeability are not yet well understood. In this study, the mechanical properties of a selected group of materials with different microstructures were investigated using tensile, fracture toughness, and hole-expansion tests to find the factor governing the stretch-flangeability that is universally applicable to a variety of metallic materials. It was found that the fracture toughness of materials, measured using the fracture initiation energy, is a universal factor governing stretch-flangeability. We verified that fracture toughness is the key factor governing stretch-flangeability, showing that the hole-expansion ratio could be well predicted using finite element analysis associated with a simple ductile damage model, without explicitly taking into account the microstructural complexity of each specimen. This validates the use of the fracture toughness as a key factor of stretch-flangeability.

The mechanical and thermal properties of a particulate composite are generally dependent on its microstructure. In the present work, statistical synthetic structures are built to represent three-dimensional microstructure of SiC particle-reinforced Al composites. The statistical parameters are referred to the previous literature and our experimental data. Thermo-mechanical finite element analysis is performed to predict the Young’s modulus and thermal expansion coefficient, which are in good agreement with the experimental results. Furthermore, the irregular distribution of stress and strain from statistical synthetic models provides better qualitative description compared to other artificially synthesized models, such as unit cell models. Considering the accuracy, flexibility, and cost of the statistical synthetic model, we confirm that this model has high potential as an efficient modeling scheme for a wide range of materials.

Effect of precipitates on surface roughening during stretching of a synthetic microstructure that is constructed with microstructural features equivalent to those of Al6061 alloy has been investigated under crystal plasticity framework. Single crystal plasticity simulations are first conducted to examine the interplay between precipitates and the matrix with typical rolling texture components. Afterwards, a banded structure with alternating Cube and Goss orientations is simulated to see the effect of precipitates and the spatial distribution of orientations on surface roughening. Lastly, the roughening behavior of a synthetic microstructure with morphological features identical to those of Al6061 alloy observed through electron back scattered diffraction and scanning electron microscopy images is investigated with crystal plasticity simulations. Results establish that precipitates act differently depending on the spatial distribution of kinematically weak and strong orientations. This study demonstrates how precipitates interact with different orientations and affect surface roughening of a precipitate hardened microstructure with and without texture bands.

Stretch-flangeability measured using hole expansion test (HET) represents the ability of a material to form into a complex shaped component. Despite its importance in automotive applications of advanced high strength steels, stretch-flangeability is a less known sheet metal forming property. In this paper, we investigate the factors governing hole expansion ratio (HER) by means of tensile test and HET. We correlate a wide range of tensile properties with HERs of steel sheet specimens because the stress state in the hole edge region during the HET is almost the same as that of the uniaxial tensile test. In order to evaluate an intrinsic HER of steel sheet specimens, the initial hole of the HET specimen is produced using a milling process after punching, which can remove accumulated shearing damage and micro-void in the hole edge region that is present when using the standard HER evaluation method. It was found that the intrinsic HER of steel sheet specimens was proportional to the strain rate sensitivity exponent and post uniform elongation.

Stretch-flangeability representing the capability of a sheet material to form into a complex shaped part is not a well-known sheet metal forming property. We correlate mechanical properties with stretch-flangeability of various advanced high strength steels (AHSSs) to capture the stretch-flanging phenomenon and improve the stretch-flangeability of steel sheet materials. The stretch-flangeability of materials is usually evaluated using a hole expansion test. During the hole expansion test, the stress state in the hole edge part of the specimen is almost the same as that of the uniaxial tensile test. However, a single parameter in tensile properties of the AHSSs exhibits no clear correlation with flangeability estimated as the hole expansion ratio (HER). Because micro-cracks in the hole edge region of the hole expansion testing samples play a significant role in HER values, we propose and demonstrate that fracture toughness is the key factor governing the HER of AHSSs.

In this study, the heterogeneous anisotropic microstructure and mechanical properties of additively manufactured (CoCrFeMnNi)99C1 high-entropy alloy (HEA) are comprehensively investigated using experimental and theoretical analyses. For the present alloys, the selective laser melting (SLM) process produced orthogonally anisotropic microstructure with not only strong macroscopic morphological but also sharp microscopic crystallographic textures. Moreover, due to the complex thermal gradient and history in the melt pools, the columnar grains were heterogeneously evolved along the building direction with alternatively arranged layers of fine and coarse grains parallel to the laser scanning direction. This unique morphological texture played a dominant factor for the big difference in tensile properties between different loading directions in the early stage of deformation. In particular, the alternatively arrangement of fine and coarse grains could generate high hetero-deformation induced (HDI) hardening along the scanning direction in the as-built samples by profuse evolution of geometrically necessary dislocation at the boundaries of each layer. On the other hand, upon the last stage of plastic deformation, the crystallographic texture played a crucial role in directional flow behavior by modulating twinning activity. The combined contribution of the various anisotropic microstructural factors to the tensile properties of the SLM-processed HEAs was clarified both qualitatively and quantitatively. This work will shed light on effective utilization of both heterogeneity and anisotropy of the structural parts for customized performance via expanding multi-scale freedom of design in additive manufacturing.

Observations based on either side of experiment or modeling often have difficulties in understanding microstructural and mechanical evolutions during deformation, and in application to the macroscopic behavior of materials. In the present study, an integrated experimental-numerical analysis on ferrous medium-entropy alloy (FMEA) was conducted to understand the micromechanical response of the constituent phases in the FMEA at −137 °C. The initial face-centered cubic (FCC) single phase microstructure of the FMEA was transformed to body-centered cubic (BCC) martensite during tensile deformation at −137 °C, resulting in improved low-temperature mechanical properties. The microstructure evolution due to deformation-induced phase transformation mechanism and strain partitioning behavior was analyzed using ex-situ electron backscatter diffraction. The mechanical responses related to the stress partitioning between constituent phases and deformation-induced transformation rate were measured using in-situ neutron diffraction in combination with the nanoindentation analysis. Three-dimensional microstructure volume element based crystal plasticity models were built based on the experimental observations, and the simulation results were in good agreement with the experimental ones. The concurrent analysis by means of the integrated methodology revealed that the dynamic stress–strain partitioning process between the FCC and BCC martensite enables the superior strain hardening capability and the resulting outstanding low-temperature mechanical properties.

A martensitic TiNi shape memory alloy was processed by high-pressure torsion (HPT) for 1.5, 10 and 20 turns followed by post-deformation annealing (PDA) at 673 and 773 K for various times in order to study the microstructural evolution during annealing and the shape memory effect (SME). Processing by HPT followed by the optimum PDA leads to an appropriate microstructure for the occurrence of a superior SME which is attributed to the strengthening of the martensitic matrix and grain refinement. A fully martensitic structure (B19' phase) with a very small grain size is ideal for the optimum SME. The results indicate that the nanocrystalline microstructures after PDA contain a martensitic B19' phase together with an R-phase and this latter phase diminishes the SME. Applying a higher annealing temperature or longer annealing time may remove the R-phase but also reduce the SME due to grain growth and the consequent decrease in the strength of the material. The results show the optimum procedure is a short-term anneal for 10 min at 673 K or only 1.5 min at 773 K after 1.5 turns of HPT processing to produce a maximum recovered strain of ~8.4% which shows more than 50% improvement compared with the solution-annealed condition.

The local microtexture developments of Cu near the interface of Al-core/Cu-sheath composites during multi-pass caliber rolling are investigated using the finite element method in conjunction with a visco-plastic self-consistent model. Two models with different interfacial conditions between Al and Cu are used in order to investigate the effect of the interfacial condition. The resulting inverse pole figures and difference in ODFs for the different numbers of caliber rolling passes indicate that the Coulomb frictional contact condition between Al and Cu represents the final microtextures better than the fully bonded interfacial condition. Keywords: Caliber rolling, Core/sheath composite, Interface texture, Finite element method, Visco-plastic self-consistent model

In this study, porous titanium samples were manufactured by a space holder method with sodium chloride. Each porous titanium sample contained two types of pores based on their sizes: macropores and micropores. Macropores were those emerged from removing the space holder, whereas micropores were voids created during powder compaction. The porous titanium exhibited low elastic modulus close to that of the human bone. Computed tomography (CT) was employed to examine the porous structure of the Ti samples. The CT results were then used in finite element simulations for analysis of the mechanical behavior of the porous titanium. The CT-based finite element model was found to give better results compared to the unit-cell finite element model in terms of agreement with the experimental data. The CT model combined with the strain hardening behavior of Ti having micropores prescribed to the matrix allowed for accurate predictions of elastic modulus, yield strength, and flow stress. These results signify the importance of taking into account pores at different scales as well as their morphology and distribution at least at macroscale.

A martensitic NiTi shape memory alloy was processed successfully by equal-channel angular pressing (ECAP) for one pass at room temperature using a core–sheath billet design. The annealing behavior and shape memory effect of the ECAP specimens were studied followed by post-deformation annealing (PDA) at 673 K for various times. The recrystallization and structural evolution during annealing were investigated by differential scanning calorimetry, dilatometry, X-ray diffraction, transmission electron microscopy and microhardness measurements. The results indicate that the shape memory effect improves by PDA after ECAP processing. Annealing for 10 min gives a good shape memory effect which leads to a maximum in recoverable strain of 6.9 pct upon heating where this is more than a 25 pct improvement compared with the initial state.