Prediction of macroscopic damage behaviour of precipitation strengthened steels via multiscale simulations

Molnár, Dávid; Weber, Ulrich; Binkele, Peter; Rapp, Dennis; Schmauder, Siegfried

doi:10.1002/gamm.201510013

Cited by 7 publications

(3 citation statements)

References 65 publications

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“…Po et al [ 54 ] reported a review of the DDD method for numerical investigations of plasticity in crystals. Molnar et al [ 55 ] published some previous work on DDD simulations. The submicron dimension covered by DDD is the upper limit for molecular dynamics simulations [ 53 ] on the nanoscale.…”

Section: Applications Of Machine Learning Resultsmentioning

confidence: 99%

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

Schneider

Prabhu

Hoess³

et al. 2022

Materials

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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.

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Section: Applications Of Machine Learning Resultsmentioning

confidence: 99%

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

Schneider

Prabhu

Hoess³

et al. 2022

Materials

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“…Status Solidi B (2016) 15 because the circumvention proceeds with a lower effective radius. The observed effects show clearly that size, shape, and coherency of precipitates are not sufficient for predictions of precipitation strengthening in material systems with large lattice misfit.…”

Section: Original Papermentioning

confidence: 98%

“…In addition, it is intended to provide the CRSS in dependence of radius and distance between precipitates. As done in case of Fe–Cu () and Fe–Cr (), these data can be used in a multi‐scale modeling approach which extends investigations of strengthening to larger length scales.…”

Section: Introductionmentioning

confidence: 99%

Molecular dynamics simulations of strengthening due to silver precipitates in copper matrix

Hocker

Rapp

Schmauder

2017

Physica Status Solidi (b)

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Molecular dynamics simulations of edge dislocation interactions with coherent and incoherent silver precipitates in the copper matrix are applied to investigate precipitation strengthening. Simulated shear tests with spherical and octahedral precipitates revealed that dislocations can cut a precipitate or circumvent it by the Orowan mechanism. Precipitates with radii below 3thinmathspacenormalnm are cut whereas both processes were observed for radii in the range of 3–9 nm. The reason for the occurrence of the Orowan mechanism is that dislocation reactions at the interface can lead to sessile dislocations. Orowan circumvention is more likely for spheres than for octahedra which is due to different dislocation types existing at the matrix/precipitate interfaces. On average, the critical resolved shear stress is found to be slightly higher for Orowan processes. In case of small precipitates, the critical resolved shear stress depends strongly on the coherency, whereas for larger precipitates, it is mainly influenced by dislocation reactions at the interface. In some cases, the formation of a jog was observed which can reduce the critical resolved shear stress whereas it was increased significantly in the cases of pronounced cross‐slip without jog formation.

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Multiscale Simulation of Precipitation in Copper-Alloyed Pipeline Steels and in Cu-Ni-Si Alloys

Rapp

Sajadi

Molnár

et al. 2018

Handbook of Mechanics of Materials

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Prediction of macroscopic damage behaviour of precipitation strengthened steels via multiscale simulations

Cited by 7 publications

References 65 publications

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

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

Molecular dynamics simulations of strengthening due to silver precipitates in copper matrix

Multiscale Simulation of Precipitation in Copper-Alloyed Pipeline Steels and in Cu-Ni-Si Alloys

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