DRAGen – A deep learning supported RVE generator framework for complex microstructure models

Henrich, Manuel; Fehlemann, Niklas; Pütz, Felix; Neite, Maximilian; Kong, Linghao; Shen, Fuhui; Könemann, Markus; Dölz, Michael; Münstermann, Sebastian

doi:10.1016/j.heliyon.2023.e19003

Cited by 10 publications

(6 citation statements)

References 54 publications

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“…Accordingly, the present study utilizes and briefly summarizes the data from this prior publication. The figures presented herein are based on datasets originally developed for Henrich et al [11], yielding graphs that replicate the results. These figures are included in the appendix; for detailed methodologies concerning the data acquisition and analysis techniques used in these figures, readers are referred to the cited publication.…”

Section: Materials Characterization and Microstructure Reconstructionmentioning

confidence: 76%

“…The material used in this study is a NO30 electrical steel grade with a thickness of 300

m. The material has already been characterized in the publication by Henrich et al [ 11 ]. Accordingly, the present study utilizes and briefly summarizes the data from this prior publication.…”

Section: Materials Characterization and Microstructure Reconstructionmentioning

confidence: 99%

“…In the following, the core logic of

is briefly explained. For a more detailed description of the tool, the two publications by Henrich et al [ 11 , 23 ] are recommended.

is built upon three main parts, a Wasserstein Generative Adverserial Network (

) supported input generator, a discrete Random Sequential Addition (

) algorithm, and a discrete tesselation algorithm.…”

Section: Materials Characterization and Microstructure Reconstructionmentioning

confidence: 99%

“…These parameters are not always sufficient to describe materials in their overall complexity. Henrich et al [ 11 ] introduced an

generator called Discrete RVE Automation and Generation (

), which covers more than just grain-dependent parameters. One of these features is the shape of the

itself.…”

Section: Introductionmentioning

confidence: 99%

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DRAGen in Application—An Approach for Microstructural Fatigue Predictions of Non-Oriented Electrical Steel Sheets

Henrich,

Münstermann

2024

Materials

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This study investigates multiple cyclic loading scenarios of non-oriented electrical steel sheets through both experimental and numerical approaches. The numerical simulations were conducted using Representative Volume Elements generated with DRAGen. DRAGen allowed for the generation of Representative Volume Elements with a non-cubic shape to cover the complete sheet thickness and enough grains to represent the material’s texture. The experimental results, on the other hand, are utilized to calibrate and validate a prediction model, highlighting the significance of accumulated plastic slip as a suitable parameter correlated with fatigue life. Using the accumulated plastic slip from the simulations, a fatigue fracture locus is introduced, which describes a 3D surface dependent on the maximum stress, fatigue life, and the fatigue stress ratio. The study shows reliable results for the fatigue life prediction using the calibrated fatigue fracture locus. While substantial progress has been made in predicting the fatigue life at multiple fatigue stress ratios, notable disparities between experimental and simulation results suggest the need for further investigations regarding the influence of the surface quality. This observation motivates ongoing research efforts aimed at refining simulation methodologies to better incorporate surface roughness effects. In summary, this study presents a validated model for predicting fatigue life in non-oriented electrical steel sheets, offering valuable insights into material behavior at different loading scenarios and informing future research directions for enhanced structural performance and durability.

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Section: Materials Characterization and Microstructure Reconstructionmentioning

confidence: 76%

“…The material used in this study is a NO30 electrical steel grade with a thickness of 300

m. The material has already been characterized in the publication by Henrich et al [ 11 ]. Accordingly, the present study utilizes and briefly summarizes the data from this prior publication.…”

Section: Materials Characterization and Microstructure Reconstructionmentioning

confidence: 99%

“…In the following, the core logic of

is briefly explained. For a more detailed description of the tool, the two publications by Henrich et al [ 11 , 23 ] are recommended.

is built upon three main parts, a Wasserstein Generative Adverserial Network (

) supported input generator, a discrete Random Sequential Addition (

) algorithm, and a discrete tesselation algorithm.…”

Section: Materials Characterization and Microstructure Reconstructionmentioning

confidence: 99%

“…These parameters are not always sufficient to describe materials in their overall complexity. Henrich et al [ 11 ] introduced an

generator called Discrete RVE Automation and Generation (

), which covers more than just grain-dependent parameters. One of these features is the shape of the

itself.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

DRAGen in Application—An Approach for Microstructural Fatigue Predictions of Non-Oriented Electrical Steel Sheets

Henrich,

Münstermann

2024

Materials

Self Cite

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“…On the other hand, the size of RVE is large enough to contain many mesoscopic elements and sufficient mesoscopic structure information at the mesoscopic scale, so it can represent the statistical average properties of a localized continuum [7]. Thus far, there have been a lot of studies on RVEs for composite materials [8][9][10][11][12]. For instance, Zhang et al [8] presented a parameterized and automated modelling method for generating 3D orthogonal woven composite RVE geometry, including yarn geometry variations.…”

Section: Introductionmentioning

confidence: 99%

Experimental and Numerical Investigation into the Mechanical Behavior of Composite Solid Propellants Subject to Uniaxial Tension

Wu,

Jiang,

et al. 2023

Materials

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To further explore the quasi-static mechanical characteristics of composite solid propellants at low strain rates, an investigation was conducted on the mechanical behavior and damage mechanisms of a four-component hydroxy-terminated polybutadiene (HTPB) propellant by means of experiments and numerical simulation. A uniaxial tensile test and scanning electron microscope (SEM) characterization experiment were carried out. A microstructural model, which accurately represents the mesoscopic structure, was developed via the integration of micro-CT scanning and image-processing techniques. The constructed microstructural model was utilized to conduct a numerical simulation of the mechanical behavior. The experimental results demonstrated that the maximum tensile strength increases with increasing strain rate, and the primary cause of propellant failure at low strain rates is the dewetting phenomenon occurring at the interface between the larger particles and the matrix. The maximum tensile strength is 0.48 MPa when the strain rate is 0.00119 s−1, and the maximum tensile strength is 0.37 MPa when the strain rate is 0.000119 s−1. The simulation results indicated a consistent trend in variation when comparing the simulation and experimental curves. This suggested that the established model exhibits a high level of reliability, and provides a promising approach for carrying out microstructural simulations of heterogeneous propellants in future. The mechanical behavior of the propellant can be effectively described by utilizing a mesoscopic finite element model that incorporates the superelastic constitutive model of the matrix and the bilinear cohesive model. This framework facilitates the representation of mesoscopic damage evolution, which consequently provides insights into the damage mechanism. Additionally, the utilization of such models assists in compensating for the limitations of damage evolution characterization experiments.

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Fast descriptor-based 2D and 3D microstructure reconstruction using the Portilla–Simoncelli algorithm

Seibert,

Raßloff,

Kalina

et al. 2024

Engineering with Computers

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Reconstructing microstructures from statistical descriptors is a key enabler of computer-based inverse materials design. In the Yeong–Torquato algorithm and other common methods, the problem is approached by formulating it as an optimization problem in the space of possible microstructures. In this case, the error between the desired microstructure and the current reconstruction is measured in terms of a descriptor. As an alternative, descriptors can be regarded as constraints defining subspaces or regions in the microstructure space. Given a set of descriptors, a valid microstructure can be obtained by sequentially projecting onto these subspaces. This is done in the Portilla–Simoncelli algorithm, which is well known in the field of texture synthesis. Noting the algorithm’s potential, the present work aims at introducing it to microstructure reconstruction. After exploring its capabilities and limitations in 2D, a dimensionality expansion is developed for reconstructing 3D volumes from 2D reference data. The resulting method is extremely efficient, as it allows for high-resolution reconstructions on conventional laptops. Various numerical experiments are conducted to demonstrate its versatility and scalability. Finally, the method is validated by comparing homogenized mechanical properties of original and reconstructed 3D microstructures.

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DRAGen – A deep learning supported RVE generator framework for complex microstructure models

Cited by 10 publications

References 54 publications

DRAGen in Application—An Approach for Microstructural Fatigue Predictions of Non-Oriented Electrical Steel Sheets

DRAGen in Application—An Approach for Microstructural Fatigue Predictions of Non-Oriented Electrical Steel Sheets

Experimental and Numerical Investigation into the Mechanical Behavior of Composite Solid Propellants Subject to Uniaxial Tension

Fast descriptor-based 2D and 3D microstructure reconstruction using the Portilla–Simoncelli algorithm

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