Computational Materials Science Research Papers (original) (raw)

In this study an artificial neural network (ANN) model was developed to predict the oxidation behavior of magnesia graphite composites. After mechanism evaluation in different conditions, the kinetic parameters such as effective diffusion coefficient and diffusion activation energy of oxidation were calculated from ANN predicted results at different graphite content. The obtained mechanism and kinetic parameters were compared with experimental data.First of all, the reliability of the model was checked with different available data. It was found that the model results were in good agreement with experimental data prediction.The results showed that the main mechanism of oxidation was pore diffusion and effective diffusion coefficient as well as diffusion activation energy were comparable with previous works.Effective diffusion coefficient and diffusion activation energy which were calculated versus graphite content are in good agreement with experimental values.

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- by Chuying Ouyang and +2
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- Computational Materials Science
- by Lucia Reining and +1
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- Materials Engineering, Condensed Matter Physics, Computational Materials Science, Local Density Approximation

The mechanical properties and radiation tolerance of nanostructured ferritic alloys rely on a dense population of nanometer-scale Y–Ti oxides. The stability of these nano-oxides during extended service is critical in high temperature applications. Here, a model framework is developed for the thermodynamics and kinetics of Y–Ti oxide nucleation, growth and coarsening. The model, which is based upon available thermodynamic and kinetic data as well as key density functional theory calculations, shows that nano-oxide nucleation and growth are highly driven and that pipe diffusion is the dominant mode of their coarsening, in agreement with previous analyses of experimental high temperature data. The model predicts that the nano-oxides are thermally stable for 80 or more years below 1175 K. This analysis also provides insights into the effect of O and Ti on nano-oxide sizes, and on optimization of alloy microstructure.

- by Leland Barnard and +1
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- Phase Transformations, Computational Materials Science, Nuclear Materials, Precipitation
- by Jeng-Da Chai
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- Physics, Computational Physics, Quantum Physics, Chemistry

An overview of the micromechanical theoretical and numerical models of wood is presented. Different methods of analysis of the effects of wood microstructures at different scale levels on the mechanical behaviour, deformation and strength of wood are discussed and compared. Micromechanical models of deformation and strength of wood are divided into three groups: cellular models (applied most often to the mesoscale or cell scale analysis of the wood deformation), continuum micromechanics and homogenization based methods, models which consider wood as a composite and are applied mainly to the analysis of wood at the microscale (cell wall scale) level and multiscale models. Lattice and composite models, which are used to analyze the damage and fracture of wood, are considered in a separate section. The areas of applicability and strong sides of each approach are discussed.

- by Anubhav Jain
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- Materials Engineering, Condensed Matter Physics, Materials Science, Thermodynamics

The development of manufacturing technologies for new materials involves the generation of a large and continually evolving volume of information. The analysis, integration and management of such large volumes of data, typically stored in multiple independently developed databases, creates significant challenges for practitioners. There is a critical need especially for open-sharing of data pertaining to engineering design which together with effective decision support tools can enable innovation. We believe that ontology applied to engineering (OE) represents a viable strategy for the alignment, reconciliation and integration of diverse and disparate data. The scope of OE includes: consistent capture of knowledge pertaining to the types of entities involved; facilitation of cooperation among diverse group of experts; more effective ongoing curation, and update of manufacturing data; collaborative design and knowledge reuse. As an illustrative case study we propose an ontology focused

- by Francesco Furini and +1
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- Ontology, Computational Materials Science, Computer Aided Design, Materials Science and Engineering

In present study, the tribological behavior of hybrid composites with A356 aluminum alloy matrix reinforced with 10 wt.% of SiC and 5 wt.% of graphite was investigated using the Taguchi method. The composites were produced by the compocasting procedure. The tribological properties were studied using block-on-disk tribometer under lubricated sliding conditions at different normal loads (40N, 80N and 120N), sliding speeds (0.25 m/s, 0.5 m/s and 1 m/s) and sliding distances (150 m, 300 m and 1200 m). Analysis of the wear rate results was performed using the ANOVA technique. The lowest level of wear rate corresponded to the contact conditions with normal load of 40N, sliding speed of 1.0 m/s and sliding distance of 1200 m.

- by Blaza Stojanovic and +1
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- Engineering, Materials Engineering, Mechanical Engineering, Materials Science

With the development and commercialization of the recyclebot (plastic extruders that fabricate 3-D printing filament from recycled or virgin materials) and various syringe pump designs for self-replicating rapid prototypers (RepRaps), the material selection available for consumers who produce products using 3-D printer operators is expanding rapidly. This paper provides an open-source algorithm for identifying prior art for 3-D printing materials. Specifically this paper provides a new approach for determining obviousness in this technology area. The potential ramifications on both innovation and patent law in the 3-D printing technological space are discussed.

- by Joshua Pearce
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- Materials Engineering, Materials Science, Law, Intellectual Property

Self-cleaning properties have received significant attention for the importance of their potential. Coatings at Nano-scales offer possibilities of using materials for self-cleaning surfaces. Recent efforts have begun to focus on the kinds of materials including metals, semiconductors and polymers. Such materials can have enormous potential in only a few applications. Moreover, the production of these materials requires high costs with low photo activity. In this regard, TiO 2 and its derived materials have shown acceptable and effective suggestions for this application. Moreover, the mechanism of self-cleaning has been explained by the effect of hydrophilic and hydrophobic. Hydrophilic and hydrophobic can have many applications in different areas like water purification, microfluidics and photovoltaic. In this review, the application of self-cleaning in solar cells and environment as well as TiO 2 derived materials and their applications in water management have been briefly illustrated. In addition, it has been explained that a huge number of self-cleaning materials, applications and improvement in utilities have been essential. In short, we have conducted a comprehensive review of the new approach and to mention numerous materials with hydrophobic and hydrophilic properties would be promising for most environmental concerns. Bio-inspired surface respond in nature through hydrophobic (Cicada Wing, Butterfly Wing, Lotus Leaf, Rice Leaf) and hydrophilic (Fish Scale, Snail Shell, Shark Skin) properties was divided in 4 and 3 respectively. Anti reflective coatings with self-cleaning properties have drowned considerable attention for both their basic appearances and vast applied usages. Antireflective coatings with self-cleaning properties have been considered because of their fascinating features and vast diversity of empirical uses.

Here we present cellular automaton models in materials science. It gives an introduction to the fundamentals of cellular automata and reviews applications, particularly for those that predict recrystallization phenomena. Cellular automata for recrystallization are typically discrete in time, physical space, and orientation space and often use quantities such as dislocation density and crystal orientation as state variables. Cellular automata can be defined on a regular or nonregular two- or three-dimensional lattice considering the first, second, and third neighbor shell for the calculation of the local driving forces. The kinetic transformation rules are usually formulated to map a linearized symmetric rate equation for sharp grain boundary segment motion. While deterministic cellular automata directly perform cell switches by sweeping the corresponding set of neighbor cells in accord with the underlying rate equation, probabilistic cellular automata calculate the switching probability of each lattice point and make the actual decision about a switching event by evaluating the local switching probability using a Monte Carlo step. Switches are in a cellular automaton algorithm generally performed as a function of the previous state of a lattice point and the state of the neighboring lattice points. The transformation rules can be scaled in terms of time and space using, for instance, the ratio of the local and the maximum possible grain boundary mobility, the local crystallographic texture, the ratio of the local and the maximum-occurring driving forces, or appropriate scaling measures derived from a real initial specimen. The cell state update in a cellular automaton is made in synchrony for all cells. The review deals, in particular, with the prediction of the kinetics, microstructure, and texture of recrystallization. Couplings between cellular automata and crystal plasticity finite element models are also discussed.

In the modern practice of stamping simulation of complex industrial parts the prediction of springback still lacks accuracy. In commercial software packages various empirical constitutive laws for stamping are available. Limited to simple empirical models for material anisotropy they do not take into account in a full manner the effects of microstructure and its evolution during the deformation process. The crystal plasticity finite element method bridges the gap between the polycrystalline texture and macroscopic mechanical properties that opens the way for more profound consideration of metal anisotropy in the stamping process simulation. In this paper the application of crystal plasticity FEM within the concept of virtual material testing with a representative volume element (RVE) is demonstrated. Using virtual tests it becomes possible, for example, to determine the actual shape of the yield locus and Lankford parameters and to use this information to calibrate empirical constitutive models. Along with standard uniaxial tensile tests other strain paths can be investigated like biaxial tensile, compressive or shear tests.
The application of the crystal plasticity FEM for the virtual testing is demonstrated for DC04 and H320LA steel grades. The parameters of the Vegter yield locus are calibrated and the use case demonstration is completed by simulation of a typical industrial part in PAMSTAMP 2G.

To achieve safety and reliability in pipelines installed in seismic and permafrost regions, it is necessary to use linepipe materials with high strength and ductility. The introduction of dual-phase steels, e.g., with a bainite and dispersed martensite–austenite (MA) constituent, would provide the necessary ingredients for the improvement of the strain capacity (as required by a new strain-based linepipe design approach) and toughness. To fine-tune the alloy design and ensure these dual-phase steels have the required mechanical properties, an understanding of the governing deformation micromechanisms is essential. For this purpose, a recently developed joint numerical–experimental approach that involves the integrated use of microscopic digital image correlation analysis, electron backscatter diffraction, and multiphysics crystal plasticity simulations with a spectral solver was employed in this study. The local strain and stress evolution and microstructure maps of representative microstructural patches were captured with a high spatial resolution using this approach. A comparison of these maps provides new insights into the deformation mechanism in dual-phase microstructures, especially regarding the influence of the bainite and MA grain size and the MA distribution on the strain localization behavior.

We present a fully embedded implementation of a full-field crystal plasticity model in an implicit finite element (FE) framework, a combination which realizes a multiscale approach for the simulation of large strain plastic deformation. At each integration point of the macroscopic FE model a spectral solver, based on Fast Fourier Transforms (FFTs), feeds-in the homogenized response from an underlying full-field polycrystalline representative volume element (RVE) model which is solved by using a crystal plasticity constitutive formulation. Both, a phenome-nological hardening law and a dislocation density based hardening model, implemented in the open source software DAMASK, have been employed to provide the constitutive response at the mesoscale. The accuracy of the FE-FFT model has been benchmarked by one-element tests of several loading scenarios for an FCC polycrystal including simple tension, simple compression, and simple shear. The multiscale model is applied to simulate four application cases, i.e., plane strain deformation of an FCC plate, compression of an FCC cylinder, four-point bending of HCP bars, and beam bending of a dual-phase steel. The excellent capabilities of the model to predict the microstructure evolution at the mesoscale and the mechanical responses at both macroscale and mesoscale are demonstrated.

The reduction of iron ore with carbon-carriers is one of the largest sources of greenhouse gas emissions in the industry, motivating global activities to replace the coke-based blast furnace reduction by hydrogenbased direct reduction (HyDR). Iron oxide reduction with hydrogen has been widely investigated both experimentally and theoretically. The HyDR process includes multiple types of chemical reactions, solid state and defect-mediated diffusion (of oxygen and hydrogen species), several phase transformations, as well as massive volume shrinkage and mechanical stress buildup. However, studies focusing on the chemo-mechanical interplay during the reduction reaction influenced by microstructure are sparse. In this work, a chemo-mechanically coupled phase-field (PF) model has been developed to explore the interplay between phase transformation, chemical reaction, species diffusion, large elasto-plastic deformation and microstructure evolution. Energetic constitutive relations of the model are based on the system free energy which is calibrated with the help of a thermodynamic database. The model has been first applied to the classical core-shell (wüstite-iron) structure. Simulations show that the phase transformation from wüstite to α-iron can result in high stresses and rapidly decelerating reaction kinetics. Mechanical stresses create elastic energy in the system, an effect which can negatively influence the phase transformations, thus causing slow reaction kinetics and low metallization. However, if the elastic stress becomes comparatively high, it can shift the shape of the free energy from a double-well to a single-well case, speed up the transformation and result in a higher reduction degree compared to the low-stress doublewell case. The model has been applied to simulate an experimentally characterized iron oxide specimen with its complex microstructure. The observed microstructure evolution during reduction is well predicted by the model. The simulation results also show that isolated pores in the microstructure are filled with water vapor during reduction, which can influence the local reaction atmosphere and dynamics.

A dislocation density-based crystal plasticity model incorporating both transformation-induced plasticity (TRIP) and twinning-induced plasticity (TWIP) is presented. The approach is a physically-based model which reflects microstructure investigations of ε-martensite, twins and dislocation structures in high manganese steels. Validation of the model was conducted using experimental data for a TRIP/TWIP Fe-22Mn-0.6C steel. The model is able to predict, based on the difference in the stacking fault energies, the activation of TRIP and/or TWIP deformation mechanisms at different temperatures.

High entropy alloys (HEA) are multicomponent (5 or more) massive solid solutions with an equiatomic or a near equiatomic composition. The original ideal of investigating multicomponent alloys in equal or near-equal proportions represents a new alloy exploration strategy. Instead of starting from a corner of a phase diagram with one prevalent base element, it has been suggested that new materials could be identified by directly producing equiatomic compositions with multiple components. The term ‘‘high entropy alloys’’ was introduced by Yeh et al., based on the hypothesis that the high configurational entropy would stabilize the solid solution phase over competing intermetallic and elemental phases. A well-studied HEA is the Cantor alloy i.e. Co20Cr20Fe20Mn20Ni20 (at.%) which develops a single phase fcc solid solution. Recently, it has been shown that a non-equiatomic composition of this alloy system also exhibits a single phase fcc solid solution irrespective of its slightly lower mixing entropy. The objective of this study is two-fold. One focus is the prediction and analysis of the phase stability of this alloy system i.e. FexMn62ÿxNi30Co6Cr2 (at.%, x = 22, 27, 32, 37, and 42), while varying the Fe and Mn contents, and maintaining the compositions of Cr, Co and Ni constant. The configurational entropy of these alloys ranges from 1.295 to 1.334 kB/atom (kB is the Boltzmann constant) which yields 80–83% of that in equiatomic composition (1.6094 kB/atom) as shown in Fig. 1. Another focus is to explore the feasibility of using the CALPHAD (CALculation of PHAse Diagrams) method for future knowledge based approaches to the design of HEAs. Compared with other approaches for designing HEAs (e.g. empirical rules, or ab initio based methods), the CALPHAD method provides an optimal balance between efficiency and accuracy. On the other hand, most multicomponent systems are not fully covered by the available CALPHAD databases. Instead, current CALPHAD simulations of multicomponent systems are based on the extrapolation from binary, ternary, and, (perhaps) quaternary systems. Hence, the accuracy of the corresponding predictions yielded by
using a CALPHAD approach needs to be critically evaluated.
The objective of this study is to experimentally and theoretically investigate the phase stability of non-equiatomic FexMn62ÿxNi30Co6Cr2 based high entropy alloys, where x ranges from 22 to 42 at.%. Another aim is to systematically and critically assess the predictive capability of the CALPHAD approach for such high entropy alloy systems. We find that the CALPHAD simulations provide a very consistent assessment of phase stability yielding good agreement with experimental observations. These include the equilibrium phase formation at high temperatures, the constituent phases after non-equilibrium solidification processes, unfavorable segregation profiles inherited from solidification together with the associated nucleation and growth of low temperature phases, and undesired martensitic transformation effects. Encouraged by these consistent theoretical and experimental results, we extend our simulations to other alloy systems with equiatomic compositions reported in the literature. Using these other equiatomic model systems we demonstrate how systematic CALPHAD simulations can improve and accelerate the design of multicomponent alloy systems.

- by M. Duancheng and +1
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- Thermodynamics, Computational Materials Science, Segregation, Electron Backscatter Diffraction (EBSD)

We present structural, elastic, electronic and optical properties of the perovskites SrMO3 (M=Ti, and Sn) for different pressure. The computational method is based on the pseudo-potential plane wave method (PP-PW). The exchange-correlation energy is described in the generalized gradient approximation (GGA). The calculated equilibrium lattice parameters are in reasonable agreement with the available experimental data. This work shows that the perovskites SrTiO3, and SrSnO3 are mechanically stable and present an indirect band gaps at the Fermi level. Applied pressure does not change the shape of the total valence electronic charge density and most of the electronic charge density is shifted toward O atom. Furthermore, in order to understand the optical properties of SrMO3, the dielectric function, absorption coefficient, optical reflectivity, refractive index, extinction coefficient and electron energy-loss are calculated for radiation up to 80 eV. The enhancement of pressure decreases the dielectric function and refractive indices of SrTiO3 and SrSnO3.

We present a virtual laboratory to investigate the anisotropic yield behavior of polycrystalline materials by using high resolution crystal plasticity simulations. Employing a fast spectral method solver enables us to conduct a large number of full-field virtual experiments with different stress states to accurately identify the yield surface of the probed materials. Based on the simulated yield stress points, the parameters for many commonly used yield functions are acquired simultaneously with a nonlinear least square fitting procedure. Exemplarily, the parameters of four yield functions frequently used in sheet metal forming, namely Yld91, Yld2000-2D, Yld2004-18p, and Yld2004-27p are adjusted to accurately describe the yield behavior of an AA3014 aluminum alloy at two material states, namely with a recrystallization texture and a cold rolling texture. The comparison to experimental results proves that the methodology presented, combining accuracy with efficiency, is a promising micromechanics-based tool for probing the mechanical anisotropy of polycrystalline metals and for identifying the parameters of advanced yield functions.

Many applications involve actuated devices made of shape memory alloys, but the lack of efficient numerical tools hinders the development of such technologies. Software using a finite element method like ANSYS allows the user to predict complex responses of a system without extensive programming. In this paper, a homemade phenomenological 1D bilinear model is programmed through the USERMAT procedure in

The electrochemical properties of high strength 7xxx aluminium alloys strongly depend on the substitutional occupancy of Zn by Cu and Al in the strengthening η-phase with the two-sublattice structure, and its microstructural and compositional prediction is the key to design of new generation corrosion resistant alloys. In this work, we have developed a chemical-potential-based phase-field model capable of describing multi-component and two-sublattice ordered phases, during commercial multi-stage artificial ageing treatments, by directly incorporating the compound energy CALPHAD formalism. The model developed has been employed to explore the complex compositional pathway for the formation of the η-phase in Al-Zn-Mg-Cu alloys during heat treatments. In particular, the influence of alloy composition, solute diffusivity, and heat treatment parameters on the microstructural and compositional evolution of η-phase precipitates, was systematically investigated from a thermodynamic and kinetic perspective and compared to electron probe microanalysis validation data. The simulated η-phase growth kinetics and the matrix residual solute evolution in the AA7050 alloy indicates that Zn depletion mainly controlled the η-phase growth process during the early stage of ageing, resulting in fast η-phase growth kinetics, enrichment of Zn in the η-phase, and an excess in residual Cu in the matrix. The gradual substitution of Zn by Cu atoms in the η-phase during the later ageing stage was in principle a kinetically controlled process, owing to the slower diffusivity of Cu relative to Zn in the matrix. It was also found that the higher nominal Zn content in alloys like the AA7085 alloy, compared to the AA7050 alloy, could significantly enhance the chemical potential of Zn, but this had a minor influence on Cu, which essentially led to the higher Zn content (and consequently lower Cu) seen in the η-phase. Finally, substantial depletion of Zn and supersaturation of Cu in the matrix of the AA7050 alloy was predicted after 24 h ageing at 120 • C , whereas the second higher-temperature ageing stage at 180 • C markedly enhanced the diffusion of Cu from the supersaturated matrix into the η-phase, while the matrix residual Zn content was only slightly affected.