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An efficient production platform for converting mannitol to valencene was established in the evolved S. cerevisiae cell factory.

The highly tunable properties of multi-principal element alloys, commonly known as high-entropy alloys (HEAs), provide a remarkable potential for the development of superior materials for critical structural applications that involve extreme conditions. However, the optimization of the properties of HEAs has been primarily limited to behavior at either low or high temperatures. Here, we report on a non-equiatomic, heterostructured, high-entropy alloy FeNiCoAlTaB which possesses remarkable combinations of mechanical properties across a wide range of temperatures from 77 K to 1073 K. The current metastable alloy presents good ductility and superior engineering tensile strengths of 2.2 GPa, 1.4 GPa, 800 MPa, and 500 MPa at 77 K, 298 K, 873 K, and 1073 K, respectively. This behavior is achieved by a synergic sequence of individual mechanisms that are activated at different temperatures. The alloy even displays pseudoelasticity at 77 K with an applied load up to 2 GPa. This work provides a methodology for tailoring structural heterogeneity and metastability in the design and fabrication of multifunctional HEAs that will outperform known metals and alloys over a wide range of temperatures.

Refractory high-entropy alloys (RHEAs) are considered promising candidate materials for high-temperature structural applications that currently use superalloys. However, for most of the reported RHEAs, their poor ductility and negligible cold-workability at room temperature have hindered their use. Here, we report a new class of non-equiatomic NbTaTi-based RHEAs that can be cold-rolled to a reduction of over 90% from the as-cast state without surface treatment and/or intermediate annealing. This excellent cold-workability is facilitated by activation of a high-density of dislocations and deformation twins as well as by high diffusivity paths, such that these RHEAs can be rendered homogeneous at lower annealing temperatures for much shorter time durations. In addition, we report that the RHEAs retain their high strength at elevated temperatures and exhibit considerable ductility at cryogenic conditions, evading the traditional strength–ductility trade-off. This class of super-formable RHEAs provides a novel design pathway to fabricate high-temperature structural materials via an energy- and time-saving method.

A new simulation-based pattern-matching method is developed to retrieve the deformation tensor from electron backscatter diffraction patterns. Minimizing the least-squares difference between a target pattern and simulated deformed patterns, the deformation state of the target pattern can be inferred with an average absolute error of ∼ 10 − 8 for the distortion tensor under ideal conditions. The new approach is robust against significant pattern rotation compared to the cross-correlation based method. Additionally, the pattern center can be simultaneously optimized with an average absolute distortion tensor error of ∼ 10 − 4 for noise-free patterns.

Machine learning, especially deep learning, has been extensively applied and studied in the area of machine health monitoring. For machine health monitoring systems (MHMS), major efforts have been put into designing and deploying more and more complex machine learning models. Those black-box models are nontransparent toward their working mechanism. However, this research trend brings huge potential risks in real life. Since machine health monitoring itself belongs to high stake decision applications, the outputs of the autonomous monitoring systems should be trustworthy and reliable, which refers to obtain explainability. Then, it comes to the following key question: why the deployed MHMS predicts what they predict . In this article, we shed some light on this meaningful research direction: explainable MHMSs (EMHMS). In EMHMS, the machine doctor could act like a real doctor who can not only make a diagnosis but also describe the patient’s symptoms. First, we propose a specific convolutional neural network (CNN) structure, named DecouplEd Feature-Temporal CNN (DEFT-CNN), to balance precision–explainability tradeoff. Specifically, feature information and temporal information have been encoded in different stages of our model. The spatial attention module is added to boost the performance of the model. Then, to explain the decision of the model, we adopt gradient-based methods to generate features and temporal saliency maps highlighting which kinds of features and time steps are keys for the model’s predictions. Finally, we conduct the experimental studies on two real datasets to verify the effectiveness of our proposed framework.

A novel non-equiatomic NbTaTi-based refractory complex concentrated alloy (RCCA) capable of being cold-rolled up to a reduction in thickness of over 90% directly from the as-cast state is studied. Instead of conventional heat-treatment at temperatures greater than 0.8Tm for long durations, the excellent cold-workability in this RCCA allows for tunable microstructural features, including fine-grained, coarse-grained, and heterogeneous lamella (HL) structures, through proper heat-treatment at relatively lower temperatures for shorter duration. By tailoring microstructures, balanced tensile properties are achieved in this RCCA, through back-stress strengthening of HL structures. This study demonstrates an energy-saving and efficient way to fabricate high-performance RCCAs.

High-entropy alloys containing multi-principal-element systems significantly expand the potential alloy design space, and offer the possibility of overcoming the strength-ductility trade-off in metallurgical research. However, the gain in ultra-high strength through traditional grain refinement and precipitation-strengthening mechanisms inevitably leads to a drastic loss of ductility. Here, we report on the design and fabrication of heterogeneous-lamella structured, aged bulk high-entropy alloy, which attains gigapascal tensile strength while retaining excellent ductility (UTS ~1.4 GPa, elongation ~30%; UTS ~1.7 GPa, elongation ~10%). Our work shows that the improved strength-ductility synergy arises due to various complementary strengthening mechanisms, including solid-solution, interfaces, precipitation and martensitic transformation, which influence the hardening and deformation processes at different strain levels. In particular, the hetero-deformation that is associated with the formation of microbands as well as the stress-induced martensite promotes additional hardening and hence high ductility. The strategy described here, that is leveraging the concept of heterogeneous microstructure design, provides a practical and novel method for fabricating high-performance structural materials.

FeAl-based Metallic-Intermetallic Laminate (MIL) composites exhibit enhanced strength and ductility compared to previously studied MIL composites. The deformation and fracture evolution of the FeAl-based MIL composites are investigated here via incremental compression testing. Microstructure assessment via electron backscatter diffraction suggests that deformation proceeds in a fairly homogeneous manner across gradients in the microstructure. Eventual failure is mainly induced by normal stresses, whereas other MIL composites typically fail by shear induced localizations. Geometrically necessary dislocation analysis indicates the FeAl regions deform in similar manners for the three MIL composites (Fe-FeAl-MIL, 430SS-FeAl-MIL, and 304SS-FeAl-MIL), and each fails in a similar mode. While the FeAl phase is the majority constituent of the composites, the mechanical properties are significantly influenced by the softer metal layers. The transition layer formed between the Fe-based metal layers and FeAl regions is the most critical constituent of the composites. Although the volume fraction of the transition layer is only ∼15%, a stronger transition layer can improve the work hardening behavior of the FeAl phase, increasing MIL composite strength by as much as 1 GPa. The findings can guide the design of the MIL composites to achieve even better mechanical properties.

The emergence of commercial electron backscatter diffraction (EBSD) equipment ushered in an era of information rich maps produced by determining the orientation of user-selected crystal structures. Since then, a technological revolution has occurred in the quality, rate detection, and analysis of these diffractions patterns. The next revolution in EBSD is the ability to directly utilize the information rich diffraction patterns in a high-throughput manner. Aided by machine learning techniques, this new methodology is, as demonstrated herein, capable of accurately separating phases in a material by crystal symmetry, chemistry, and even lattice parameters with fewer human decisions. This work is the first demonstration of such capabilities and addresses many of the major challenges faced in modern EBSD. Diffraction patterns are collected from a variety of samples, and a convolutional neural network, a type of machine learning algorithm, is trained to autonomously recognize the subtle differences in the diffraction patterns and output phase maps of the material. This study offers a path to machine learning coupled phase mapping as databases of EBSD patterns encompass an increasing number of the possible space groups, chemistry changes, and lattice parameter variations.

Accurately determining the crystallographic structure of a material, organic or inorganic, is a critical primary step in material development and analysis. The most common practices involve analysis of diffraction patterns produced in laboratory XRD, TEM, and synchrotron X-ray sources. However, these techniques are slow, require careful sample preparation, can be difficult to access, and are prone to human error during analysis. This paper presents a newly developed methodology that represents a paradigm change in electron diffraction-based structure analysis techniques, with the potential to revolutionize multiple crystallography-related fields. A machine learning-based approach for rapid and autonomous identification of the crystal structure of metals and alloys, ceramics, and geological specimens, without any prior knowledge of the sample, is presented and demonstrated utilizing the electron backscatter diffraction (EBSD) technique. Electron backscatter diffraction patterns are collected from materials with well-known crystal structures, then a deep neural network model is constructed for classification to a specific Bravais lattice or point group. The applicability of this approach is evaluated on diffraction patterns from samples unknown to the computer without any human input or data filtering. This is in comparison to traditional Hough transform EBSD, which requires that you have already determined the phases present in your sample. The internal operations of the neural network are elucidated through visualizing the symmetry features learned by the convolutional neural network. It is determined that the model looks for the same features a crystallographer would use, even though it is not explicitly programmed to do so. This study opens the door to fully automated, high-throughput determination of crystal structures via several electron-based diffraction techniques.

As essential components in intelligent systems, printed soft electronics (PSEs) are playing crucial roles in public health, national security, and economics. Innovations in printing technologies are required to promote the broad application of high-performance PSEs at a low cost. However, current printing techniques are still facing long-lasting challenges in addressing the conflict between printing speed and performance. To overcome this challenge, we developed a new corona-enabled electrostatic printing (CEP) technique for ultra-fast (milliseconds) roll-to-roll (R2R) manufacturing of binder-free multifunctional e-skins. The printing capability and controllability of CEP were investigated through parametric studies and microstructure observation. The electric field generation, material transfer, and particle amount and size selecting mechanisms were numerically and experimentally studied. CEP-printed graphene e-skins were demonstrated to possess an outstanding strain sensing performance. The binder-free feature of the CEP-assembled networks enables them to provide pressure sensitivity as low as 2.5 Pa and capability to detect acoustic signals of hundreds of hertz in frequency. Furthermore, the CEP technique was utilized to pattern different types of functional materials (e.g., graphene and thermochromic polymers) onto different substrates (e.g., tape and textile). Overall, this study demonstrated that CEP can be a novel contactless and ultra-fast manufacturing platform compatible with the R2R process for fabricating high-performance, scalable, and low-cost soft electronics.

This paper reports on a heterogeneous-structured β-Ti alloy with an exceptional combination of high strength and ductility, resulting from optimized hierarchical features in a lamellar microstructure. The microstructure is achieved by controlling a fraction of coarse/fine domains, spatial grain-size distribution, and different types of grain boundaries. The large degree of microstructural heterogeneity leads to obvious mechanical incompatibility and strain partitioning during plastic deformation. In addition, experimental results demonstrate that the unique heterogeneous structure induces high hetero-deformation induced hardening and promotes dislocation accumulation, thereby achieving a high yield strength ∼970 MPa and uniform tensile elongation of ∼16%.

A new method based on conventional thermomechanical processing is proposed for design of non-equiatomic FeNiCoAlTaB (NCATB) high entropy alloy (HEA) with multiscale heterogeneous lamella (HL) structures to achieve outstanding mechanical properties. Two kinds of microstructures are produced in this study: 1) specimens annealed at 1050 °C exhibit structure composed of fine grains and ultrafine grains, which exhibits a combination of high strength (UTS ~1050 MPa) and good ductility (elongation ~23%); and 2) specimens annealed at 1200 °C which exhibit structure composed of fine grains and coarse grains, which demonstrates excellent synergy of good strength (UTS ~890 MPa) and exceptional ductility (elongation ~43%). It has been shown that shear bands formed in the cold rolling step are responsible for the fine-grain area in both specimens. Additionally, precipitation of NiAl (B2) particles also affects the formation of HL structures due to Zener pinning effect. The back-stress strengthening mechanism, responsible for high strength and ductility, is verified through combined “loading-unloading-reloading” (LUR) cyclic tensile tests and electron backscatter diffraction (EBSD) based geometrically necessary dislocation (GND) density analysis.

The precipitation behavior of tantalum (Ta) and NiAl along grain boundaries has been investigated in polycrystalline Fe-28.5Ni-17.5Co-11.5Al-2.5Ta-0.05B (NCATB) (at%) alloy in order to understand their role in limiting its superelastic behavior. It is found that Ta precipitates at grain boundary triple-junctions first, then along grain boundaries, and acts as nuclei for β-NiAl phase formation. Small amounts of boron is also found distributed adjacent to the Ta precipitates. Consequently, the martensitic transformation associated with the superelastic response is promoted along grain boundaries by the Ta and β-NiAl precipitated there. Furthermore, increasing aging temperature facilitates the precipitation of Ta and β-NiAl along grain boundaries. The mean cell width of precipitates along grain boundaries significantly affects the fracture mode and ductility of NCATB specimens. Both the fraction of low-angle boundaries and the intensity of recrystallization texture are significantly increased with increasing reduction in thickness of cold-rolled specimens over 95%, which is effective for the suppression of Ta and β-NiAl formation as their formation occurs primarily on high angle grain boundaries. As a result, the superelastic behavior of the FeNiCoAlTaB alloy is closely linked to the reduction in thickness, which increases the ratio of low angle to high angle boundaries, and hence the propensity for grain boundary failure associated with the grain boundary precipitates and grain boundary initiated martensitic transformation.

In recent years, Internet of Things (IoT) has become one of the most important technologies in academia and industry. However, in many application scenarios, smart devices are distributed over a remote area without terrestrial communication systems. Considering the power of smart devices is limited, it is infeasible for terrestrial access networks to effectively receive the data of smart devices. Therefore, as a supplement to the terrestrial networks, Space-Air-Ground networks are critical to the Internet of Remote Things (IoRT). In this paper, we use high-altitude platforms (HAPs) to assist data transmission from smart devices to low earth orbit (LEO) satellites. Aiming at minimizing system power consumption, we propose an algorithm that jointly optimizes the resource allocation scheme and the HAP relay deployment. Since the problem is a mix-integer non-convex programming which is prohibitive to solve, our proposed algorithm divides it into two sub-problems to find a near-optimal solution with low computational complexity. Simulation results show that compared with average resource allocation scheme, our proposed algorithm can significantly reduce the system power consumption while satisfying the rate requirements of smart devices.

Extreme deformation of high-entropy alloys increases toughness by converting crystalline structures to amorphous zones., Ever-harsher service conditions in the future will call for materials with increasing ability to undergo deformation without sustaining damage while retaining high strength. Prime candidates for these conditions are certain high-entropy alloys (HEAs), which have extraordinary work-hardening ability and toughness. By subjecting the equiatomic CrMnFeCoNi HEA to severe plastic deformation through swaging followed by either quasi-static compression or dynamic deformation in shear, we observe a dense structure comprising stacking faults, twins, transformation from the face-centered cubic to the hexagonal close-packed structure, and, of particular note, amorphization. The coordinated propagation of stacking faults and twins along {111} planes generates high-deformation regions, which can reorganize into hexagonal packets; when the defect density in these regions reaches a critical level, they generate islands of amorphous material. These regions can have outstanding mechanical properties, which provide additional strengthening and/or toughening mechanisms to enhance the capability of these alloys to withstand extreme loading conditions.

Global optimization algorithms have been adopted to the simultaneously refinement of orientation and pattern center for electron backscatter diffraction patterns as well as deformation state extraction. The hyperparameter space and mutation schemes of differential evolution (DE) algorithm has been thoroughly investigated and showed to be a more efficient algorithm than the particle swarm optimization (PSO) algorithm. The optimal hyperparameters for DE generally depend on conditions such as the number of variables to be optimized and the size of bounded search space but reasonably close initial values for crossover probability is 0.9, mutation factor is 0.5, population size is ten times the number of variables, and number of iterations is at least 100. Validation on a set of simulated undeformed single crystal nickel patterns reveals a mean accuracy of ≈ 0 . 03 ° and ≈ 0.01% detector width across a large field of view. In addition, validation using noisy simulated deformed patterns with known deformation state and pattern center shows that the mean accuracy of shear strain and rotation components is ≈ 0.001 and for the normal strain ≈ 0.002.

Recently, graphene has provided unprecedent progress in device performance at the atom limit. High performance of field-effect transistors (FETs) requires a low graphene-metal contact resistance. However, the chemical doping methods used to tailor or improve the properties of graphene are sensitive to ambient conditions. Here, we fabricate a single layer perfluorinated polymeric sulfonic acid (PFSA), also known as Nafion, between graphene and the substrate as a p-type dopant. The PFSA doping method, without inducing any additional structural defects, reduces the contact resistance of graphene by ~28.8%, which has a significant impact on practical applications. This reduction can keep at least 67 days due to the extreme stability of PFSA. Effective, uniform and stable, the PFSA doping method provides an efficient way to reduce the contact resistance of graphene applications.

Millimeter wave (mmWave) communications are considered as a key technology for 5G due to the huge spectrum resources which can't be provided in traditional frequency band. However, traditional full digital precoding methods are difficult to implement in massive multiple-input-multiple-output (MIMO) systems due to high cost and high power consumption. Nevertheless, mmWave hybrid precoding architecture achieves a good balance between performance and cost. Inspired by the matching pursuit (MP) algorithm, we propose a near-optimal hybrid precoding algorithm for fully-connected hybrid architecture. Instead of searching the analog vectors from the set of the array response vectors, we successively construct the appropriate analog precoding vectors by the alternating iteration method to constitute the analog precoding matrix, and the baseband precoding matrix is derived by regular least squares. In addition, the proposed algorithm takes no assumptions or approximations. Numerical simulations show that the proposed algorithm outperforms previous methods in performance with lower computational complexity.

The satellite cannot simultaneously serve all users due to the large number of users on the earth. User grouping algorithm, which different users are divided into several groups to be served consecutively by the satellite in different time slots, is studied to cluster all customers. In this work, two greedy user grouping algorithms are proposed. Adaptive user grouping for sum-rate maximization (AUG-SM) algorithm with variable number of groups can schedule all users to guarantee the fairness between the users. Aiming at maximization sum-rate of satellite communications, certain user grouping for sum-rate maximization (CUG-SM) algorithm with certain number of groups is proposed. Simulation results show that the proposed algorithms achieve favorable sum-rate performance.

Machine learning, especially deep learning, have been extensively applied and studied in the area of machine health monitoring in the recent "big data" era. For machine health monitoring systems (MHMS), the major efforts have been put in designing and deploying more and more complex machine learning models which are also called as black-box models considering they are non-transparent towards their working mechanism. However, this research trend brings huge potential risk in real life. Since machine health monitoring itself is a high-stake decision scenario, the decision of the autonomous monitoring system should be trustworthy and reliable, which refers to obtain explainability. Then, it comes to the following key question: Why the deployed MHMS predict what they predict. In this paper, we shed some lights on this meaningful research direction: explainable machine health monitoring systems (EMHMS). In EMHMS, the machine doctor could act like a real doctor who can only make diagnosis but also describe the patient’s symptoms. First, we propose a specific convolutional neural network (CNN) structure, named as DecouplEd Feature-Temporal CNN (DEFT-CNN), balances precision-explainability trade-off. Specifically, features and temporal information will be encoded in different stages of our model. Second, to explain the decision of the model, we adopt the gradient-based methods to generate features and temporal saliency maps highlighting which kind of features and time steps are key for the model’s predictions. At last, we conduct the experimental studies in the CWRU bearing fault diagnosis dataset to verify the effectiveness of our proposed framework.

Electron backscatter diffraction (EBSD) is one of the primary tools in materials development and analysis. The technique can perform simultaneous analyses at multiple length scales, providing local sub-micron information mapped globally to centimeter scale. Recently, a series of technological revolutions simultaneously increased diffraction pattern quality and collection rate. After collection, current EBSD pattern indexing techniques (whether Hough-based or dictionary pattern matching based) are capable of reliably differentiating between a “user selected” set of phases, if those phases contain sufficiently different crystal structures. EBSD is currently less well suited for the problem of phase identification where the phases in the sample are unknown. A pattern analysis technique capable of phase identification, utilizing the information-rich diffraction patterns potentially coupled with other data, such as EDS-derived chemistry, would enable EBSD to become a high-throughput technique replacing many slower (X-ray diffraction) or more expensive (neutron diffraction) methods. We utilize a machine learning technique to develop a general methodology for the space group classification of diffraction patterns; this is demonstrated within the $\lpar 4/m\comma \;\bar{3}\comma \;\;2/m\rpar$ point group. We evaluate the machine learning algorithm's performance in real-world situations using materials outside the training set, simultaneously elucidating the role of atomic scattering factors, orientation, and pattern quality on classification accuracy.

Although high-entropy materials are attracting considerable interest due to a combination of useful properties and promising applications, predicting their formation remains a hindrance for rational discovery of new systems. Experimental approaches are based on physical intuition and/or expensive trial and error strategies. Most computational methods rely on the availability of sufficient experimental data and computational power. Machine learning (ML) applied to materials science can accelerate development and reduce costs. In this study, we propose an ML method, leveraging thermodynamic and compositional attributes of a given material for predicting the synthesizability (i.e., entropy-forming ability) of disordered metal carbides. The relative importance of the thermodynamic and compositional features for the predictions are then explored. The approach’s suitability is demonstrated by comparing values calculated with density functional theory to ML predictions. Finally, the model is employed to predict the entropy-forming ability of 70 new compositions; several predictions are validated by additional density functional theory calculations and experimental synthesis, corroborating the effectiveness in exploring vast compositional spaces in a high-throughput manner. Importantly, seven compositions are selected specifically, because they contain all three of the Group VI elements (Cr, Mo, and W), which do not form room temperature-stable rock-salt monocarbides. Incorporating the Group VI elements into the rock-salt structure provides further opportunity for tuning the electronic structure and potentially material performance.

Low Earth Orbit (LEO) satellite networks play a major role to provide communication support for the regions beyond the coverage of terrestrial network systems. The positions of gateway stations have influence on the time delay, power consumption and throughput of LEO satellite networks, making it important to deploy the gateway stations at the best position. Aiming at minimizing the inter-satellite hop count, we are the first to study the deployment problem of gateway stations. The gateway deployment problem is formulated as a non-convex optimization problem. According to the characteristics of the deployment problem, we propose an improved genetic algorithm (GA) and an improved simulated annealing algorithm (SA) not only to get the optimal deployment location, but also to greatly reduce the computational complexity. Simulation results and analyses show that compared with random deployment, the optimal locations of the gateway stations obtained by the proposed algorithms can decrease 42.6% average hop count of inter-satellite links and increase 4.4% average capacity of feeder links.

Electron backscatter diffraction (EBSD) is a powerful technique capable of multiple analyses on a bulk sample; however, Hough- based EBSD requires user selected phases and a look-up table of interplanar angles. The challenges of phase differentiation and identification remain largely unsolved. We present a general methodology for incorporating rapid and autonomous identification of the crystal structure utilizing a machine learning- based approach trained to differentiate EBSD patterns. To demonstrate its utility, the model is evaluated on diffraction patterns from new materials. The internal operations of the neural network are elucidated through visualization, and it is apparent that the model relies on the same symmetry features a crystallographer would use.

A micromechanical model for the shrinkage anisotropy during sintering of metallic powders is proposed and experimentally assessed. The framework developed for modeling sintering based on the mechanism of grain boundary diffusion is extended to take into account the dislocation pipe-enhanced volume diffusion. The studied iron powder samples are pre-shaped into their green forms by uniaxial cold pressing before sintering step. The resultant green bodies are anisotropic porous structures, with inhomogeneous plastic deformation at the inter-particle contacts. These non-uniformities are considered to be the cause of the anisotropic dislocation pipe diffusion mechanisms, and thus of the undesired shape distortion during shrinkage. The proposed model describes the shrinkage rates in the compaction loading and transverse directions, as functions of both structural and geometric activities of the samples. Dislocation densities can be estimated from such equations using dilatometry and image analysis data. The reliability and applicability of the developed modeling framework are verified by comparing the calculated dislocation densities with outcomes of nanoindentation and electron backscatter diffraction-derived lattice rotations.

Formation of strong recrystallization texture in new non-equiatomic Fe-Ni-Co-Al-based high entropy alloys has been investigated. Optimal annealing temperature and time to obtain strong texture is subsequently determined through microstructure and recrystallization texture study of cold-rolled NCACB (34.95Fe-27.5Ni-17.5Co-11.5Al-8.5Cr-0.05B at.%) at 1200 °C and 1300 °C for different annealing times. It has also been found that the recrystallization texture is influenced by grain growth and the relative grain size (d/t, d-grain size, t-sheet thickness). Furthermore, contribution of chemical composition to texture has been examined in two additional cold-rolled non-equiatomic high entropy alloys, NCAB (Fe-27.5Ni-17.5Co-11.5Al-0.05B at.%) and NCATB (Fe-27.5Ni-17.5Co-11.5Al-2.5Ta-0.05B at.%), under the same optimal annealing condition used for NCACB. Comparison of texture intensity of NCAB, NCATB, and NCACB demonstrates that Cr is very effective in the formation of strong recrystallization texture of Fe-Ni-Co-Al-based alloys. Based on low-angle boundary statistics, it is hypothesized that decrease in stacking fault energy is responsible for the enhanced recrystallization texture in NCACB. Additionally, it is demonstrated employing a Zener-type model for the pinning of grain boundaries by second-phase particles, that precipitated NiAl particles facilitate the abnormal grain growth in NCACB. The tensile tests results show that strong recrystallization texture and bamboo-like structure improve the ductility of NCACB.

The nature of dislocation generation as a function of applied plastic strain in quasi-statically compressed polycrystalline pure nickel has been studied experimentally at ambient temperature. First, to ensure representative datasets of the geometrically-necessary dislocation densities (ρGND) associated with non-uniform plastic deformation, measurements over large (several millimeter square) areas were made using Hough-based EBSD methods. In addition, the total dislocation density (ρT) responsible for the overall work hardening is estimated from the measured flow stress based on Taylor's hardening model. Next, the statistically stored dislocation (SSD) density (ρSSD) is calculated by subtracting the GND density from the total dislocation density. The results demonstrate that in quasi-statically deformed nickel: i) the measured GND density varies linearly as a function of plastic strain in the range between 0.05 and 0.46; although Ashby's model predicts linearity for GND density evolution over entire range of strains, this study does not cover strains below 0.05; ii) the SSD density increases at a rate much faster than GND density; and iii) the SSD density exceeds the GND density at above 0.09 plastic strain. Both i) and ii) are in agreement with Ashby's prediction, while the magnitudes of GND density (iii) differ from Ashby's model prediction, particularly at large applied strains. Overall, this study enables the interplay of GNDs and SSDs in the hardening of nickel to be gleaned in a quantitative sense. This study illustrates that GNDs are the more important for the strength of polycrystalline metals in the early stages of work hardening, whereas SSDs contribute more to the strength at larger strains. Over the range of strain in this study, work hardening is predominantly through rapid multiplication of SSDs; whereas the GND density is initially higher than SSD density at 0.05 probably due to non-linear evolution of GNDs at low strains (

Thermomechanical-cycling processes were applied to a metastable Ti-23Nb-0.7Ta-2Zr-1.0O (at%) alloy to control a volume fraction of mechanical twins, resulting from activation of twinning-induced plasticity effects. Analysis of the microstructure features designed using electron backscattering and X-ray diffraction revealed the deformation bands induced by the cycling process were characterized as only {332} β twinning. It was also observed that the fraction of twins was significantly increased with increasing the number of the cycles in the process, resulting in a pronounced softening effect. To shed a light on the relevant micro-scale features (e.g., geometric orientation and stress concentrations) responsible for the microstructure evolution and mechanical response, the local Schmid factor of grains and geometrically-necessary dislocation densities were evaluated from experimentally observed results, and correlated with deformation twinning. A good correlation between the local geometric Schmid factor and softening behavior, due to texture evolution within twins was established, as well as the effect of local stress concentrations on microstructure evolution of this Ti-Nb Gum metal. Significant decrease in micro-hardness (approximately 18% from untwinned structure) was achieved by highly twinned structure, and a possible deformation mechanism was established to enhance ductility without evident elastic properties variation in metastable β Ti alloys.