arXiv subjects

Stefan Sandfeld

Publications and source records attributed to Stefan Sandfeld.

At least 37 records · Page 2Linked to original sources

arXiv2023

Unsupervised Learning of Nanoindentation Data to Infer Microstructural Details of Complex Materials

In this study, Cu-Cr composites were studied by nanoindentation. Arrays of indents were placed over large areas of the samples resulting in datasets consisting of several hundred measurements of Young's modulus and hardness at varying indentation depths. The unsupervised learning technique, Gaussian mixture model, was employed to analyze the data, which helped to determine the number of "mechanical phases" and the respective mechanical properties. Additionally, a cross-validation approach was introduced to infer whether the data quantity was adequate and to suggest the amount of data required for reliable predictions -- one of the often encountered but difficult to resolve issues in machine learning of materials science problems.

cs.LG↗

arXiv2023

Instance Segmentation of Dislocations in TEM Images

Quantitative Transmission Electron Microscopy (TEM) during in-situ straining experiment is able to reveal the motion of dislocations -- linear defects in the crystal lattice of metals. In the domain of materials science, the knowledge about the location and movement of dislocations is important for creating novel materials with superior properties. A long-standing problem, however, is to identify the position and extract the shape of dislocations, which would ultimately help to create a digital twin of such materials. In this work, we quantitatively compare state-of-the-art instance segmentation methods, including Mask R-CNN and YOLOv8. The dislocation masks as the results of the instance segmentation are converted to mathematical lines, enabling quantitative analysis of dislocation length and geometry -- important information for the domain scientist, which we then propose to include as a novel length-aware quality metric for estimating the network performance. Our segmentation pipeline shows a high accuracy suitable for all domain-specific, further post-processing. Additionally, our physics-based metric turns out to perform much more consistently than typically used pixel-wise metrics.

cs.CV↗

arXiv2023

Deep Learning of Crystalline Defects from TEM images: A Solution for the Problem of "Never Enough Training Data"

Crystalline defects, such as line-like dislocations, play an important role for the performance and reliability of many metallic devices. Their interaction and evolution still poses a multitude of open questions to materials science and materials physics. In-situ TEM experiments can provide important insights into how dislocations behave and move. During such experiments, the dislocation microstructure is captured in form of videos. The analysis of individual video frames can provide useful insights but is limited by the capabilities of automated identification, digitization, and quantitative extraction of the dislocations as curved objects. The vast amount of data also makes manual annotation very time consuming, thereby limiting the use of Deep Learning-based, automated image analysis and segmentation of the dislocation microstructure. In this work, a parametric model for generating synthetic training data for segmentation of dislocations is developed. Even though domain scientists might dismiss synthetic training images sometimes as too artificial, our findings show that they can result in superior performance, particularly regarding the generalizing of the Deep Learning models with respect to different microstructures and imaging conditions. Additionally, we propose an enhanced deep learning method optimized for segmenting overlapping or intersecting dislocation lines. Upon testing this framework on four distinct real datasets, we find that our synthetic training data are able to yield high-quality results also on real images-even more so if fine-tune on a few real images was done.

cs.CV↗

arXiv2023

Statistical analysis of Discrete Dislocation Dynamics simulations: initial structures, cross-slip and microstructure evolution

Over the past decades, discrete dislocation dynamics simulations have been shown to reliably predict the evolution of dislocation microstructures for micrometer-sized metallic samples. Such simulations provide insight into the governing deformation mechanisms and the interplay between different physical phenomena such as dislocation reactions or cross-slip. This work is focused on a detailed analysis of the influence of the cross-slip on the evolution of dislocation systems. A tailored data mining strategy using the ``\ab*{discrete-to-continuous} framework'' allows to quantify differences and to quantitatively compare dislocation structures. We analyze the quantitative effects of the cross-slip on the microstructure in the course of a tensile test and a subsequent relaxation to present the role of cross-slip in the microstructure evolution. The precision of the extracted quantitative information using D2C strongly depends on the resolution of the domain averaging. We also analyze how the resolution of the averaging influences the distribution of total dislocation density and curvature fields of the specimen. Our analyzes are important approaches for interpreting the resulting structures calculated by dislocation dynamics simulations.

cond-mat.mtrl-sci↗

arXiv2022

Data-Mining of In-Situ TEM Experiments: Towards Understanding Nanoscale Fracture

The lifetime and performance of any engineering component, from nanoscale sensors to macroscopic structures, are strongly influenced by fracture processes. Fracture itself is a highly localized event; originating at the atomic scale by bond breaking between individual atoms close to the crack tip. These processes, however, interact with defects such as dislocations or grain boundaries and influence phenomena on much larger length scales, ultimately giving rise to macroscopic behavior and engineering-scale fracture properties. This complex interplay is the fundamental reason why identifying the atomistic structural and energetic processes occurring at a crack tip remains a longstanding and still unsolved challenge. We develop a new analysis approach for combining quantitative in-situ observations of nanoscale deformation processes at a crack tip with three-dimensional reconstruction of the dislocation structure and advanced computational analysis to address plasticity and fracture initiation in a ductile metal. Our combinatorial approach reveals details of dislocation nucleation, their interaction process, and the local internal stress state, all of which were previously inaccessible to experiments. This enables us to describe fracture processes based on local crack driving forces on a dislocation level with a high fidelity that paves the way towards a better understanding and control of local failure processes in materials.

cond-mat.mtrl-sci↗

arXiv2022

Data-mining of In-Situ TEM Experiments: on the Dynamics of Dislocations in CoCrFeMnNi Alloys

High entropy alloys are a class of materials with many significant improvements in terms of mechanical properties as compared to ``classical'' alloys. The corresponding structure-property relations are not yet entirely clear, but it is commonly believed that the good mechanical performance is strongly related to dislocation interactions with the complex energy landscape formed due to alloying. Although in-situ Transmission Electron Microscopy (TEM) allows high-resolution studies of the structure and dynamics of moving dislocations and makes the local obstacle/energy ``landscape'' directly visible in the geometry of dislocations; such observation, however, are merely qualitative, and detailed three-dimensional analyses of the interaction between dislocations and the energy landscape is still missing. In this work, we utilized dislocations as ``probes'' for the local energy maxima which play the role of pinning points for the dislocation movement. To this end, we developed a unique data-mining approach that can perform coarse-grained spatio-temporal analysis, making ensemble averaging of a considerable number of snapshots possible. We investigate the effect of pinning points on the dislocation gliding behavior of CoCrFeMnNi alloy during in-situ TEM straining and find that (i) the pinning point strength changes when dislocations glide through and (ii) the pinning point moves along the direction close to the Burgers vector direction. Our data-mining method can be applied to dislocation motion in general, making it a useful tool for dislocation research.

cond-mat.mtrl-sci↗

arXiv2022

Automated analysis of continuum fields from atomistic simulations using statistical machine learning

Atomistic simulations of the molecular dynamics/statics kind are regularly used to study small scale plasticity. Contemporary simulations are performed with tens to hundreds of millions of atoms, with snapshots of these configurations written out at regular intervals for further analysis. Continuum scale constitutive models for material behavior can benefit from information on the atomic scale, in particular in terms of the deformation mechanisms, the accommodation of the total strain and partitioning of stress and strain fields in individual grains. In this work we develop a methodology using statistical data mining and machine learning algorithms to automate the analysis of continuum field variables in atomistic simulations. We focus on three important field variables: total strain, elastic strain and microrotation. Our results show that the elastic strain in individual grains exhibits a unimodal log-normal distribution, whilst the total strain and microrotation fields evidence a multimodal distribution. The peaks in the distribution of total strain are identified with a Gaussian mixture model and methods to circumvent overfitting problems are presented. Subsequently, we evaluate the identified peaks in terms of deformation mechanisms in a grain, which e.g., helps to quantify the strain for which individual deformation mechanisms are responsible. The overall statistics of the distributions over all grains are an important input for higher scale models, which ultimately also helps to be able to quantitatively discuss the implications for information transfer to phenomenological models.

cond-mat.mtrl-sci↗

arXiv2022

Sintering of Alumina Nanoparticles: Comparison of Interatomic Potentials, Molecular Dynamics Simulations, and Data Analysis

Sintering of alumina nanoparticles is of interest both from the view of fundamental research as well as for industrial applications. Atomistic simulations are tailor-made for understanding and predicting the time- and temperature-dependent sintering behaviour. However, the quality and predictability of such analysis is strongly dependent on the performance of the underlying interatomic potentials. In this work, we investigate and benchmark four empirical interatomic potentials and discuss the resulting properties and drawbacks based on experimental and density functional theory data from the literature. The potentials, which have different origins and formulations, are then used in molecular dynamics simulations to perform a systematic study of the sintering process. To analyse the results, we develop a number of tailored data analysis approaches that are able to characterise and quantify the sintering process. Subsequently, the disparities in the sintering behaviour predicted by the potentials are critically discussed. Finally, we conclude by providing explanations for the differences in performance of the potentials, together with recommendations for molecular dynamics sintering simulations of alumina.

cond-mat.mtrl-sci↗

arXiv2021

Orientation-related twinning and dislocation glide in a Cantor High Entropy Alloy at room and cryogenic temperature studied by in situ TEM straining

In situ straining experiments were performed in a TEM on an equimolar CoCrFeMnNi (Cantor) high entropy alloy at room and cryogenic temperature. Perfect and partial dislocation activity were recorded in both cases. Twinning directly follows the development of partial dislocation shearing that has various origins (perfect dislocation splitting, anchoring). It is shown that, although twinning is more frequently observed at liquid nitrogen temperature, its prevalence depends mainly on crystal orientation. As a result, twinning and perfect dislocation plasticity are likely to occur jointly in random oriented polycrystals, even at early stages of deformation.

cond-mat.mtrl-sci↗

arXiv2021

OptiMic: A tool to generate optimized polycrystalline microstructures for materials simulations

Polycrystal microstructures, with their distinct physical, chemical, structural and topological entities, play an important role in determining the effective properties of materials. Particularly for computational studies, the well-known Voronoi tessellation technique is regularly used for obtaining microstructures. Standard Voronoi tessellations, however, exhibit statistics that are generally far removed from those in real microstructures. Nevertheless, such tessellations can be optimized to obtain certain key features and statistics seen in real microstructures. In this work, we develop the open-source software package OptiMic that enables the generation of optimized microstructures for both finite element as well as atomistic simulations. OptiMic allows for both monodispersive grains as well as irregular grains obtained currently via Voronoi tessellations. These initial microstructures can then be optimized to reflect desired statistical features. A key feature of the tool is that it gives the user extensive control on the optimization process via customizable cost functions. The software currently performs tessellations with the Voronoi method and can be easily extended to include other methods like grain-growth, phase-field etc.

cond-mat.mtrl-sci↗

arXiv2021

Steps towards a Dislocation Ontology for Crystalline Materials

The field of Materials Science is concerned with, e.g., properties and performance of materials. An important class of materials are crystalline materials that usually contain ``dislocations'' -- a line-like defect type. Dislocation decisively determine many important materials properties. Over the past decades, significant effort was put into understanding dislocation behavior across different length scales both with experimental characterization techniques as well as with simulations. However, for describing such dislocation structures there is still a lack of a common standard to represent and to connect dislocation domain knowledge across different but related communities. An ontology offers a common foundation to enable knowledge representation and data interoperability, which are important components to establish a ``digital twin''. This paper outlines the first steps towards the design of an ontology in the dislocation domain and shows a connection with the already existing ontologies in the materials science and engineering domain.

cond-mat.mtrl-sci↗

arXiv2020

Data-mining of dislocation microstructures: concepts for coarse-graining of internal energies

Continuum models of dislocation plasticity require constitutive closure assumptions, e.g., by relating details of the dislocation microstructure to energy densities. Currently, there is no systematic way for deriving or extracting such information from reference simulations, such as discrete dislocation dynamics or molecular dynamics. Here, a novel data-mining approach is proposed through which energy density data from systems of discrete dislocations can be extracted. Our approach relies on a systematic and controlled coarse-graining process and thereby is consistent with the length scale of interest. For data-mining, a range of different dislocation microstructures that were generated from 2D and 3D discrete dislocation dynamics simulations, are used. The analyses of the data sets result in energy density formulations as function of various dislocation density fields. The proposed approach solves the long-standing problem of voxel-size dependent energy calculation during coarse graining of dislocation microstructures. Thus, it is crucial for any continuum dislocation dynamics simulation.

cond-mat.mtrl-sci↗

arXiv2020

Multiscale study of the dynamic friction coefficient due to asperity plowing

A macroscopically nominal flat surface is rough at the nanoscale level and consists of nanoasperities. Therefore, the frictional properties of the macroscale-level rough surface are determined by the mechanical behaviors of nanoasperity contact pairs under shear. In this work, we first used molecular dynamics simulations to study the non-adhesive shear between single contact pairs. Subsequently, to estimate the friction coefficient of rough surfaces, we implemented the frictional behavior of a single contact pair into a Greenwood-Williamson-type statistical model. By employing the present multiscale approach, we used the size, rate, and orientation effects, which originated from nanoscale dislocation plasticity, to determine the dependence of the macroscale friction coefficient on system parameters, such as the surface roughness and separation, loading velocity, and direction. Our model predicts an unconventional dependence of the friction coefficient on the normal contact load, which has been observed in nanoscale frictional tests. Therefore, this model represents one step toward understanding some of the relevant macroscopic phenomena of surface friction at the nanoscale level.

cond-mat.mes-hall↗

arXiv2020

Predicting the flow stress and dominant yielding mechanisms: analytical models based on discrete dislocation plasticity

Dislocations are the carriers of plasticity in crystalline materials. Their collective interaction behavior is dependent on the strain rate and sample size. In small specimens, details of the nucleation process are of particular importance. In the present work, discrete dislocation dynamics (DDD) simulations are performed to investigate the dominant yielding mechanisms in single crystalline copper pillars with diameters ranging from 100 to 800 nm. Based on our simulations with different strain rates and sample size, we observe a transition of the relevant nucleation mechanism from "dislocation multiplication" to "surface nucleation". Two physics-based analytical models are established to quantitatively predict this transition, showing a good agreement for different strain rates with our DDD simulation data and with available experimental data. Therefore, the proposed analytical models help to understand the interplay between different physical parameters and nucleation mechanisms and are well suitable to estimate the material strength for different material properties and under given loading conditions.

cond-mat.mtrl-sci↗

arXiv2018

Shear relaxation behind the shock front in $\langle$110$\rangle$ Molybdenum - From the Atomic Scale to Continuous Dislocation Fields

In this work we study shock-induced plasticity in Mo single crystals, impacted along the <110> crystal orientation. In particular, the shear relaxation behind the shock front is quantitatively inspected. Molecular dynamics (MD) simulations are employed to simulate the deformation during shock, followed by post-processing to identify and quantify the dislocation lines nucleated behind the shock front. The information on the dislocation lines is ensemble averaged inside slabs of the simulation box and over different realizations of the MD simulations, from which continuous dislocation fields are extracted using the Discrete-to-Continuous method. The continuous dislocation fields are correlated with the stress and strain fields obtained from the MD simulations. Based on this analysis, we show that the elastic precursor overshoots the shear stress, after which dislocations on a specific group of slip planes are nucleated, slightly lagging behind the elastic front. Consequently, the resolved shear stress is relaxed, but the principal lateral stress increases. The latter leads to an increase in the resolved shear stress on a plane parallel to the shock wave, resulting in an additional retarded front of dislocation nucleation on planes parallel to the shock front. Finally, the two-stage process of plasticity results in an isotropic stress state in the plane parallel to the shock wave. The MD simulation results are employed to calculate the dislocation densities on specific slip planes and the plastic deformation behind the shock, bridging the gap between the information on the atomic scale and the continuum level.

cond-mat.mtrl-sci↗

arXiv2018

Size and Disorder Effects in Elasticity of Cellular Structures: From Discrete Models to Continuum Representations

Open cellular solids usually possess random microstructures that may contain a characteristic length scale, such as the cell size. This gives rise to size dependent mechanical properties where large systems behave differently from small systems. Furthermore, these structures are often irregular, which not only affects the size dependent behaviour but also leads to significant property variations among different microstructure realizations. The computational model for cellular microstructures is based on networks of Timoshenko beams. It is a computationally efficient approach allowing to obtain statistically representative averages from computing large numbers of realizations. For detailed analysis of the underlying deformation mechanisms an energetically consistent continuization method was developed which links the forces and displacements of discrete beam networks to equivalent spatially continuous stress and strain fields. This method is not only useful for evaluation and visualization purposes but also allows to perform ensemble averages of, e.g., continuous stress patterns - an analysis approach which is highly beneficial for comparisons and statistical analysis of microstructures with respect to different degrees of structural disorder.

cond-mat.mtrl-sci↗

arXiv2017

Nanoscratching of iron: a novel approach to characterize dislocation microstructures

A new approach for characterizing the dislocation microstructure obtained from atomistic simulations is introduced, which relies on converting properties of discrete lines to continuous data. This data is represented by a number of density and density-like field variables containing detailed information about properties of the dislocation microstructure. Applying this methodology to atomistic simulations of nanoscratching in iron reveals a pronounced "length scale effect": With increasing scratching length the number of dislocations increases but the density of geometrically necessary dislocations remains constant resulting in decreasing shear stress. During scratching dislocations are mostly generated at the scratch front. The nucleation rate versus scratching length has an approximately antisymmetric shape with respect to the scratch front leading to an almost constant curvature.

cond-mat.mtrl-sci↗

arXiv2017

A continuum approach to combined $γ/γ'$ evolution and dislocation plasticity in Nickel-based superalloys

Creep in single crystal Nickel-based superalloys has been a topic of interest since decades, and nowadays simulations are more and more able to complement experiments. In these alloys, the $γ/γ'$ phase microstructure co-evolves with the system of dislocations under load, and understanding the mutual interactions is essential for understanding the resulting creep properties. Predictive modeling thus requires multiphysics frameworks capable of modeling and simulating both the phase and defect microstructures. To do so, we formulate a coupled model of phase-field evolution and continuum dislocation dynamics which adequately accounts for both statistically stored and geometrically necessary dislocations. The simulated $γ/γ'$ phase microstructure with four $γ'$ variants and co-evolving dislocation microstructure is found to be in good agreement with experimental observations. The creep strain curve is obtained as a natural by-product of the microstructure evolution equations without the need for additional parameter fitting. We perform simulations of $γ/γ'$ evolution for different dislocation densities and establish the driving forces for microstructure evolution by analyzing in detail the changes in different contributions to the elastic and chemical energy density. Together with comparisons between simulated and experimental creep curves this investigation reveals the mechanisms controlling the process of directional coarsening (rafting) and demonstrates that the kinetics of rafting significantly depends on characteristics of the dislocation microstructure. In addition to rafting under constant load, we investigate the effect of changes in loading conditions and explore the possibility of improving creep properties by pre-rafting along a different loading path.

cond-mat.mtrl-sci↗