FEM Simulations of Fatigue Crack Initiation in the Oligocrystalline Microstructure of Stents.

For over two decades, vascular stents have been widely used to treat clogged vessels, serving as a scaffold to enlarge the narrowed lumen and recover the arterial flow area. High-purity oligocrystalline austenitic steel is usually applied for the production of stents. Despite the popularity and benefit of stenting, it still may cause serious clinical adverse issues, such as in-stent restenosis and stent fracture. Therefore, the study of the mechanical properties of stents and in particular the prediction of their life cycles are in the focus of materials research. In our contribution, within the finite element method, a two-scale model of crack initiation in the microstructure of stents is elaborated. The approach is developed on the basis of the physically based Tanaka-Mura model (TMM), considering the evolution of shear bands during the crack initiation phase. The model allows for the analysis of the microstructure with respect to the life cycles of real materials. The effects of different loading conditions, grain orientation, and thickness of the specimen on Wöhler curves were analysed. It was found that the microstructural features of oligocrystals are very sensitive to different loading conditions with respect to their fatigue behaviour and play a major role in fatigue crack initiation. Different grain-orientation distributions result in qualitative and quantitative differences in stress distribution and in the number of cycles for crack initiation. It was found that presence of a neutral zone in the cut-out of the microstructure under three-point-bending loading conditions changes the qualitative and quantitative patterns of stress distribution and affects the number of cycles for crack initiation. It was found that under both tensile and bending loading conditions, thicker specimens require more cycles for crack initiation. The Wöhler curves for crack initiation in oligocrystalline microstructures of stents could be compared with the ones in the experiment, taking into account that for high cyclic fatigue (HCF), typically, more than 70% of the cycles refer to crack initiation. The developed numerical tools could be used for the material design of stents.

Investigation of Auxetic Structural Deformation Behavior of PBAT Polymers Using Process and Finite Element Simulation.

The current work investigates the auxetic tensile deformation behavior of the inversehoneycomb structure with 5 × 5 cells made of biodegradable poly(butylene adipate-coterephthalate) (PBAT). Fused deposition modeling, an additive manufacturing method, was used to produce such specimens. Residual stress (RS) and warpage, more or less, always exist in such specimens due to their layer-by-layer fabrication, i.e., repeated heating and cooling. The RS influences the auxetic deformation behavior, but its measurement is challenging due to its very fine structure. Instead, the finite-element (FE)-based process simulation realized using an ABAQUS plug-in numerically predicts the RS and warpage. The predicted warpage shows a negligibly slight deviation compared to the design topology. This process simulation also provides the temperature evolution of a small-volume material, revealing the effects of local cyclic heating and cooling. The achieved RS serves as the initial condition for the FE model used to investigate the auxetic tensile behavior. With the outcomes from FE calculation without consideration of the RS, the effect of the RS on the deformation behavior is discussed for the global force-displacement curve, the structural Poisson's ratio evolution, the deformed structural status, the stress distribution, and the evolution, where the first three and the warpage are also compared with the experimental results. Furthermore, the FE simulation can easily provide the global stress-strain flow curve with the total stress calculated from the elemental stresses.

Deformation Behavior Investigation of Auxetic Structure Made of Poly(butylene adipate-co-terephthalate) Biopolymers Using Finite Element Method.

Auxetic structures made of biodegradable polymers are favorable for industrial and daily life applications. In this work, poly(butylene adipate-co-terephthalate) (PBAT) is chosen for the study of the deformation behavior of an inverse-honeycomb auxetic structure manufactured using the fused filament fabrication. The study focus is on auxetic behavior. One characteristic of polymer deformation prediction using finite element (FE) simulation is that no sounded FE model exists, due to the significantly different behavior of polymers under loading. The deformation behavior prediction of auxetic structures made of polymers poses more challenges, due to the coupled influences of material and topology on the overall behavior. Our work presents a general process to simulate auxetic structural deformation behavior for various polymers, such as PBAT, PLA (polylactic acid), and their blends. The current report emphasizes the first one. Limited by the state of the art, there is no unified regulation for calculating the Poisson's ratio ν for auxetic structures. Here, three calculation ways of ν are presented based on measured data, one of which is found to be suitable to present the auxetic structural behavior. Still, the influence of the auxetic structural topology on the calculated Poisson's ratio value is also discussed, and a suggestion is presented. The numerically predicted force-displacement curve, Poisson's ratio evolution, and the deformed auxetic structural status match the testing results very well. Furthermore, FE simulation results can easily illustrate the stress distribution both statistically and local-topology particularized, which is very helpful in analyzing in-depth the auxetic behavior.

Deformation Behavior of 3D Printed Auxetic Structures of Thermoplastic Polymers: PLA, PBAT, and Blends.

Auxetic structures have a negative Poisson's ratio and therefore expand transversely to the direction of loading instead of tapering. This unique behavior is not caused by the materials used, but by the structure, and thus offers completely new functionalities and design possibilities. As a rule, auxetic structures have a very complex geometry, which makes cost-effective production possible only by means of additive manufacturing processes. Due to the high design freedom of the strand deposition method, it makes sense to manufacture auxetic structures using this process. Therefore, in this project, polylactide acid (PLA), polybutylene adipate terephthalate (PBAT), and blends of the two polymers were produced and characterized. Filaments of the two polymers and a blend were extruded, processed into auxetic structures by strand deposition process (SDP), and investigated for their properties, primarily their Poisson's ratio. The Poisson's ratio was determined and the influence of the material on it was identified. A specific number of 5 × 5 unit cells has been found to be ideal for investigation. Dual printed specimens showed a similar auxetic behavior as the specimens made of pure PBAT. Likewise, multiple loading and unloading of the structure is possible. Furthermore, in-situ computed tomography revealed the detailed characterization of the initial state, including the warpage of the structures, damage, and traced auxetic behavior in detail.

Hybrid Data-Driven Deep Learning Framework for Material Mechanical Properties Prediction with the Focus on Dual-Phase Steel Microstructures.

A comprehensive approach to understand the mechanical behavior of materials involves costly and time-consuming experiments. Recent advances in machine learning and in the field of computational material science could significantly reduce the need for experiments by enabling the prediction of a material's mechanical behavior. In this paper, a reliable data pipeline consisting of experimentally validated phase field simulations and finite element analysis was created to generate a dataset of dual-phase steel microstructures and mechanical behaviors under different heat treatment conditions. Afterwards, a deep learning-based method was presented, which was the hybridization of two well-known transfer-learning approaches, ResNet50 and VGG16. Hyper parameter optimization (HPO) and fine-tuning were also implemented to train and boost both methods for the hybrid network. By fusing the hybrid model and the feature extractor, the dual-phase steels' yield stress, ultimate stress, and fracture strain under new treatment conditions were predicted with an error of less than 1%.

Microstructural Modeling and Simulation of a Carbon Black-Based Conductive Polymer-A Template for the Virtual Design of a Composite Material.

Carbon black is the most frequently applied conductive additive in rubber and polymer composites. In this work, we show how a carbon black microstructure in a polymer matrix can be conclusively modeled based on carbon black aggregation as well as an agglomeration mechanism using a state-of-the-art mathematical model. This novel and flexible microstructural modeling method enables us to virtually investigate the morphology of conductive additives within a polymer matrix and can be adapted to many conductive polymer combinations used for different applications. Furthermore, we calculate the electrical conductivity of the composite using a finite volume-based as well as a discrete element-based simulation technique and validate the results with experimental data. Utilizing a novel discrete element method (DEM) modeling technique, we were able to improve calculation times by a factor of 12.2 compared to finite volume method (FVM) simulations while maintaining high accuracy. Using this approach, we are able to predict the required carbon black content and minimize the amount of additive to create a polymer composite with a designated target conductivity.

Many-Scale Investigations of Deformation Behavior of Polycrystalline Composites: II-Micro-Macro Simultaneous FE and Discrete Dislocation Dynamics Simulation.

The current work numerically investigates commercial polycrystalline Ag/17vol.%SnO2 composite tensile deformation behavior with available experimental data. Such composites are useful for electric contacts and have a highly textured initial material status after hot extrusion. Experimentally, the initial sharp fiber texture and the number of Σ3-twins were reduced due to tensile loading. The local inhomogeneous distribution of hardness and Young's modulus gradually decreased from nanoindentation tests, approaching global homogeneity. Many-scale simulations, including micro-macro simultaneous finite element (FE) and discrete dislocation dynamics (DDD) simulations, were performed. Deformation mechanisms on the microscale are fundamental since they link those on the macro- and nanoscale. This work emphasizes micromechanical deformation behavior. Such FE calculations applied with crystal plasticity can predict local feature evolutions in detail, such as texture, morphology, and stress flow in individual grains. To avoid the negative influence of boundary conditions (BCs) on the result accuracy, BCs are given on the macrostructure, i.e., the microstructure is free of BCs. The particular type of 3D simulation, axisymmetry, is preferred, in which a 2D real microstructural cutout with 513 Ag grains is applied. From FE results, Σ3-twins strongly rotated to the loading direction (twins disappear), which, possibly, caused other grains to rotate away from the loading direction. The DDD simulation treats the dislocations as discrete lines and can predict the resolved shear stress (RSS) inside one grain with dependence on various features as dislocation density and lattice orientation. The RSS can act as the link between the FE and DDD predictions.

Many-Scale Investigations of the Deformation Behavior of Polycrystalline Composites: I-Machine Learning Applied for Image Segmentation.

Our work investigates the polycrystalline composite deformation behavior through multiscale simulations with experimental data at hand. Since deformation mechanisms on the micro-level link the ones on the macro-level and the nanoscale, it is preferable to perform micromechanical finite element simulations based on real microstructures. The image segmentation is a necessary step for the meshing. Our 2D EBSD images contain at least a few hundred grains. Machine learning (ML) was adopted to automatically identify subregions, i.e., individual grains, to improve local feature extraction efficiency and accuracy. Denoising in preprocessing and postprocessing before and after ML, respectively, is beneficial in high quality feature identification. The ML algorithms used were self-developed with the usage of inherent code packages (Python). The performances of the three supervised ML models-decision tree, random forest, and support vector machine-are compared herein; the latter two achieved accuracies of up to 99.8%. Calculations took about 0.5 h from the original input dataset (EBSD image) to the final output (segmented image) running on a personal computer (CPU: 3.6 GHz). For a realizable manual pixel sortation, the original image was firstly scaled from the initial resolution 10802 pixels down to 3002. After ML, some manual work was necessary due to the remaining noises to achieve the final image status ready for meshing. The ML process, including this manual work time, improved efficiency by a factor of about 24 compared to a purely manual process. Simultaneously, ML minimized the geometrical deviation between the identified and original features, since it used the original resolution. For serial work, the time efficiency would be enhanced multiplicatively.

Characterization of Cure Behavior in Epoxy Using Molecular Dynamics Simulation Compared with Dielectric Analysis and DSC.

The curing behavior of a thermosetting material that influences the properties of the material is a key issue for predicting the changes in material properties during processing. An empirical equation can describe the reaction kinetics of the curing behavior of an investigated material, which is usually estimated using experimental methods. In this study, the curing process of an epoxy resin, the polymer matrix in an epoxy molding compound, is computed concerning thermal influence using molecular dynamics. Furthermore, the accelerated reaction kinetics, which are influenced by an increased reaction cutoff distance, are investigated. As a result, the simulated crosslink density with various cutoff distances increases to plateau at a crosslink density of approx. 90% for the investigated temperatures during curing time. The reaction kinetics are derived according to the numerical results and compared with the results using experimental methods (dielectric analysis and differential scanning calorimetry), whereby the comparison shows a good agreement between experiment and simulation.

Experimental Investigations of Micro-Meso Damage Evolution for a Co/WC-Type Tool Material with Application of Digital Image Correlation and Machine Learning.

Commercial Co/WC/diamond composites are hard metals and very useful as a kind of tool material, for which both ductile and quasi-brittle behaviors are possible. This work experimentally investigates their damage evolution dependence on microstructural features. The current study investigates a different type of Co/WC-type tool material which contains 90 vol.% Co instead of the usual <50 vol.%. The studied composites showed quasi-brittle behavior. An in-house-designed testing machine realizes the in-situ micro-computed tomography (µCT) under loading. This advanced equipment can record local damage in 3D during the loading. The digital image correlation technique delivers local displacement/strain maps in 2D and 3D based on tomographic images. As shown by nanoindentation tests, matrix regions near diamond particles do not possess higher hardness values than other regions. Since local positions with high stress are often coincident with those with high strain, diamonds, which aim to achieve composites with high hardnesses, contribute to the strength less than the WC phase. Samples that illustrated quasi-brittle behavior possess about 100-130 MPa higher tensile strengths than those with ductile behavior. Voids and their connections (forming mini/small cracks) dominant the detected damages, which means void initiation, growth, and coalescence should be the damage mechanisms. The void appears in the form of debonding. Still, it is uncovered that debonding between Co-diamonds plays a major role in provoking fatal fractures for composites with quasi-brittle behavior. An optimized microstructure should avoid diamond clusters and their local volume concentrations. To improve the time efficiency and the object-identification accuracy in µCT image segmentation, machine learning (ML), U-Net in the convolutional neural network (deep learning), is applied. This method takes only about 40 min to segment more than 700 images, i.e., a great improvement of the time efficiency compared to the manual work and the accuracy maintained. The results mentioned above demonstrate knowledge about the strengthening and damage mechanisms for Co/WC/diamond composites with >50 vol.% Co. The material properties for such tool materials (>50 vol.% Co) is rarely published until now. Efforts made in the ML part contribute to the realization of autonomous processing procedures in big-data-driven science applied in materials science.

Molecular Dynamics Simulation of High-Temperature Creep Behavior of Nickel Polycrystalline Nanopillars.

As Nickel (Ni) is the base of important Ni-based superalloys for high-temperature applications, it is important to determine the creep behavior of its nano-polycrystals. The nano-tensile properties and creep behavior of nickel polycrystalline nanopillars are investigated employing molecular dynamics simulations under different temperatures, stresses, and grain sizes. The mechanisms behind the creep behavior are analyzed in detail by calculating the stress exponents, grain boundary exponents, and activation energies. The novel results in this work are summarized in a deformation mechanism map and are in good agreement with Ashby's experimental results for pure Ni. Through the deformation diagram, dislocation creep dominates the creep process when applying a high stress, while grain boundary sliding prevails at lower stress levels. These two mechanisms could also be coupled together for a low-stress but a high-temperature creep simulation. In this work, the dislocation creep is clearly observed and discussed in detail. Through analyzing the activation energies, vacancy diffusion begins to play an important role in enhancing the grain boundary creep in the creep process when the temperature is above 1000 K.

Increasing Fatigue Life of 09Mn2Si Steel by Helical Rolling: Theoretical-Experimental Study on Governing Role of Grain Boundaries.

The structure and mechanical properties of the 09Mn2Si high-strength low-alloyed steel after the five-stage helical rolling (HR) were studied. It was revealed that the fine-grained structure had been formed in the surface layer ≈ 1 mm deep as a result of severe plastic strains. In the lower layers, the "lamellar" structure had been formed, which consisted of thin elongated ferrite grains oriented in the HR direction. It was shown that the five-stage HR resulted in the increase in the steel fatigue life by more than 3.5 times under cyclic tension. The highest values of the number of cycles before failure were obtained for the samples cut from the bar core. It was demonstrated that the degree of the elastic energy dissipation in the steel samples under loading directly depended on the area of the grain boundaries as well as on the grain shapes. The fine-grained structure possessed the maximum value of the average torsional energy among all the studied samples, which caused the local material structure transformation and the decrease in the elastic energy level. This improved the crack resistance under the cyclic mechanical loading. The effect of the accumulation of the rotational strain modes at the grain boundaries was discovered, which caused the local structure transformation at the boundary zones. In the fine-grained structure, the formation of grain conglomerates was observed, which increased the values of the specific modulus of the moment of force. This could be mutually compensated due to the small sizes of grains. At the same time, the coarse-grained structures were characterized by the presence of the small number of grains with a high level of the moments of forces at their boundaries. They could result in trans-crystalline cracking.

Precipitation strengthening in Cu-Ni-Si alloys modeled with ab initio based interatomic potentials.

Effective interaction potentials suitable for Cu/δ-Ni2Si and Cu/ß-Ni3Si are developed. We optimise the potential parameters of an embedded atom method potential to reproduce forces, energies, and stresses obtained from ab initio calculations. Details of the potential generation are given, and its validation is demonstrated. The potentials are used in molecular dynamics simulations of shear tests to study the interactions of edge dislocations with coherent δ-Ni2Si and ß-Ni3Si precipitates embedded in a copper matrix. In spite of significantly different crystallographic structures of copper and δ-Ni2Si which usually result in circumvention of dislocations, we also observed cutting processes in our simulations. Dislocations cut for a specific orientation of the δ-Ni2Si precipitate and in some cases where dislocation loops originating from previous circumvention processes are present in the glide plane. It is found that ß-Ni3Si precipitates have a similar effect on precipitation strengthening as δ-Ni2Si. Dislocations usually cut ß-Ni3Si but increased coherency strain can lead to circumvention processes.

An Investigation of the Influence of Initial Roughness on the Friction and Wear Behavior of Ground Surfaces.

Friction and wear tests were performed on AISI 1045 steel specimens with different initial roughness parameters, machined by a creep-feed dry grinding process, to study the friction and wear behavior on a pin-on-disc tester in dry sliding conditions. Average surface roughness (Ra), root mean square (Rq), skewness (Rsk) and kurtosis (Rku) were involved in order to analyse the influence of the friction and wear behavior. The observations reveal that a surface with initial roughness parameters of higher Ra, Rq and Rku will lead to a longer initial-steady transition period in the sliding tests. The plastic deformation mainly concentrates in the depth of 20-50 µm under the worn surface and the critical plastic deformation is generated on the rough surface. For surfaces with large Ra, Rq, low Rsk and high Rku values, it is easy to lose the C element in, the reciprocating extrusion.

Erratum: Large-field-of-view optical elastography using digital image correlation for biological soft tissue investigation (erratum).

[This corrects the article DOI: 10.1117/1.JMI.4.1.014505.].

Large-field-of-view optical elastography using digital image correlation for biological soft tissue investigation.

In minimally invasive surgery the haptic feedback, which represents an important tool for the localization of abnormalities, is no longer available. Elastography is an imaging technique that results in quantitative elastic parameters. It can hence be used to replace the lost sense of touch, as to enable tissue localization and discrimination. Digital image correlation is the chosen elastographic imaging technique. The implementation discussed here is clinically sound, based on a spectrally engineered illumination source that enables imaging of biological surface markers (blood vessels) with high contrast. Mechanical loading and deformation of the sample is performed using a rolling indenter, which enables the investigation of large organs (size of kidney) with reduced measurement time compared to a scanning approach. Furthermore, the rolling indentation results in strain contrast improvement and an increase in detection accuracy. The successful application of digital image correlation is first demonstrated on a silicone phantom and later on biological samples. Elasticity parameters and their corresponding four-dimensional distribution are generated via solving the inverse problem (only two-dimensional displacement field and strain map experimentally available) using a well-matched hyperelastic finite element model.

Simulation of crack propagation in alumina with ab initio based polarizable force field.

We present an effective atomic interaction potential for crystalline α-Al(2)O(3) generated by the program potfit. The Wolf direct, pairwise summation method with spherical truncation is used for electrostatic interactions. The polarizability of oxygen atoms is included by use of the Tangney-Scandolo interatomic force field approach. The potential is optimized to reproduce the forces, energies, and stresses in relaxed and strained configurations as well as {0001}, {1010}, and {1120} surfaces of Al(2)O(3). Details of the force field generation are given, and its validation is demonstrated. We apply the developed potential to investigate crack propagation in α-Al(2)O(3) single crystals.

Sputter deposition of nanocrystalline beta-SiC films and molecular dynamics simulations of the sputter process.

Thin nanocrystalline films of silicon carbide (SiC) have been deposited on Si substrates by rf magnetron sputtering in pure Ar atmosphere. Simultaneously classical molecular dynamics (MD) simulations of sputtering of beta-SiC by Ar atoms were performed using IMD and Materials Explorer software with a combination of the Tersoff and the Ziegler-Biersack-Littmark (ZBL) potential in order to get more insight into the sputter process. In experiments the bias voltage (0 to -40 V) has been varied at constant substrate temperature of 900 degrees C to investigate the influence on the composition, the constitution and the mechanical properties of crystalline beta-SiC films. At second the substrate temperature has been varied between 900 degrees C and 100 degrees C to find the minimum substrate temperature that is needed to get nanocrystalline beta-SiC under the applied sputter conditions (ceramic SiC target, 300 W rf power, 18 cm target-substrate distance, 50 sccm Ar gas flow, 0.26 Pa total gas pressure). The films have been characterized by electron probe micro-analysis (EPMA), X-ray diffraction (XRD), Raman spectroscopy and atomic force microscopy (AFM). Hardness and residual stress have been investigated by nanoindentation and wafer bending. In the MD simulations the sputter yield was determined as a function of the energy of the incident Ar atoms (in the interesting range for sputter deposition, i.e., 50-1000 eV). To our knowledge this is the first time that the sputter yield of a SiC target was determined as a function of the energy of the incident Ar atoms in the low energy range by using MD simulations and compared with experimental results.