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Your search keyword '"Karimi, Ehsan"' showing total 428 results
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428 results on '"Karimi, Ehsan"'

8. Automated Segmentation of Large Image Datasets using Artificial Intelligence for Microstructure Characterisation, Damage Analysis and High-Throughput Modelling Input

Author

Medghalchi, Setareh, Kortmann, Joscha, Lee, Sang-Hyeok, Karimi, Ehsan, Kerzel, Ulrich, and Korte-Kerzel, Sandra

Subjects
Condensed Matter - Materials Science
Abstract

Many properties of commonly used materials are driven by their microstructure, which can be influenced by the composition and manufacturing processes. To optimise future materials, understanding the microstructure is critically important. Here, we present two novel approaches based on artificial intelligence that allow the segmentation of the phases of a microstructure for which simple numerical approaches, such as thresholding, are not applicable: One is based on the nnU-Net neural network, and the other on generative adversarial networks (GAN). Using large panoramic scanning electron microscopy images of dual-phase steels as a case study, we demonstrate how both methods effectively segment intricate microstructural details, including martensite, ferrite, and damage sites, for subsequent analysis. Either method shows substantial generalizability across a range of image sizes and conditions, including heat-treated microstructures with different phase configurations. The nnU-Net excels in mapping large image areas. Conversely, the GAN-based method performs reliably on smaller images, providing greater step-by-step control and flexibility over the segmentation process. This study highlights the benefits of segmented microstructural data for various purposes, such as calculating phase fractions, modelling material behaviour through finite element simulation, and conducting geometrical analyses of damage sites and the local properties of their surrounding microstructure., Comment: 37 pages, 24 figures

Published
2024

13. Rate dependence of damage formation in metallic-intermetallic Mg-Al-Ca composites

Author

Medghalchi, Setareh, Zubair, Muhammad, Karimi, Ehsan, Sandlöbes-Haut, Stefanie, Kerzel, Ulrich, and Korte-Kerzel, Sandra

Subjects
Condensed Matter - Materials Science
Abstract

We study a cast Mg-4.65Al-2.82Ca alloy with a microstructure containing $\alpha$-Mg matrix reinforced with a C36 Laves phase skeleton. Such ternary alloys are targeted for elevated temperature applications in automotive engines since they possess excellent creep properties. However, in application, the alloy may be subjected to a wide range of strain rates and in material development, accelerated testing is often of essence. It is therefore crucial to understand the effect of such rate variations. Here, we focus on their impact on damage formation. Due to the locally highly variable skeleton forming the reinforcement in this alloy, we employ an analysis based on high resolution panoramic imaging by scanning electron microscopy coupled with automated damage analysis by deep learning-based object detection and classification convolutional neural network algorithm (YOLOV5). We find that with decreasing strain rate the dominant damage mechanism for a given strain level changes: at a strain rate of $5\cdot10^{-4}/s$ the evolution of microcracks in the C36 Laves phase governs damage formation. However , when the strain rate is decreased to $5\cdot10^{-6}/s$, interface decohesion at the $\alpha$-Mg/Laves phase interfaces becomes equally important. We also observe a change in crack orientation indicating an increasing influence of plastic co-deformation of the {\alpha}-Mg matrix and Laves phase. We attribute this transition in leading damage mechanism to thermally activated processes at the interface.

Published
2023

14. Three-Dimensional Damage Characterisation in Dual Phase Steel using Deep Learning

Author

Medghalchi, Setareh, Karimi, Ehsan, Lee, Sang-Hyeok, Berkels, Benjamin, Kerzel, Ulrich, and Korte-Kerzel, Sandra

Subjects
Condensed Matter - Materials Science
Abstract

High performance sheet metals with a multi-phase microstructure suffer from deformation induced damage formation during forming in the constituent phases but importantly also where these intersect. To capture damage in terms of the physical processes in three dimensions (3D) and its stochastic nature during deformation, two challenges remain to be tackled: First, bridging high resolution analysis towards large scales to consider statistical data and, second, characterising in 3D with a resolution appropriate for sub-micron sized voids at a large scale. Here, we present how this can be achieved using panoramic scanning electron microscopy (SEM), metallographic serial sectioning, and deep-learning assisted automatic image analysis. This brings together the 3D evolution of active damage mechanisms with volumetric and environmental information for thousands of individual damage sites. We also assess potential surface preparation artefacts in 2D analyses. Overall, we find that for the material considered here, a dual phase (DP800) steel, martensite cracking is the dominant but not sole origin of deformation induced damage and that for a quantitative comparison of damage density, metallographic preparation can induce additional surface damage density far exceeding what is commonly induced between uniaxial straining steps.

Published
2023

43. Dietary administration of Bacillus subtilisimproves the health parameters and regulates the gene expression in mice receiving zearalenone-contaminated diet

Author

Marashi, Seyedehnegar, Mostarshedi, Pegah, Ghorbanikalateh, Sahel, Ghorbanikalateh, Sahar, Zoshki, Atiyeh, Taghavi, Hila, Karimi, Ehsan, Oskoueian, Ehsan, Jahromi, Mohammad Faseleh, and Shokryazdan, Parisa

Abstract

The biodegradation of mycotoxins has become a specific, efficient, and environmentally protective way to reduce the adverse effects of mycotoxins in both foods and feeds. In the current study, the effectiveness of dietary administration of Bacillus subtilison health parameters and regulated gene expression in mice receiving zearalenone zearalenone-contaminated diet was explored. In this trial, a total of twenty-four white balb/c mice were randomly assigned to three treatments. Dietary treatments were as follows: T1: The control (fed non-zearalenone-contaminated diet), T2: fed zearalenone-contaminated diet, T3: fed zearalenone-contaminated diet + Bacillus subtilisARKA-S-3 (1 × 109cfu/kg) for 28 days. The results showed, B. subtilisnotably degraded zearalenone in cultured media during 18 h incubation (p< 0.05). It significantly improved average daily weight gain and feed intake. Dietary B. subtilisnotably reduced the adverse effects of zearalenone on serum antioxidant indices (GSH-Px, SOD, ) and saved mice from oxidative stress. Also, treatments with B. subtilisimproved morphometric characteristics of the ileum ((Villus Height (µm), Villus Width (µm), and Crypt Depth (µm)) in the mice received zearalenone-contaminated diet (p< 0.05). The molecular analysis illustrated that B. subtilishas also improved the mRNA expression levels and antioxidant-related gene expression of SOD and CAT in the jejunum tissue. Moreover, it alleviated the IL-2 and IFN-γ gene profiling in the jejunum tissue. These findings illustrate that dietary administration of B. subtilisby having a degraded effect on zearalenone, possesses a protective effect on the health parameters and gene expression regulation in mice receiving a zearalenone-contaminated diet.

Published
2024
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49. Nanoliposome‐loaded phenolics from Salvia leriifolia Benth and its anticancer effects against induced colorectal cancer in mice

Author

Nasrabadi, Niusha Shojaee, primary, Vedad, Arezoo, additional, Asadi, Kimia, additional, Poorbagher, Mahsa Rastegar Moghaddam, additional, Tabrizi, Nadia Abdollahian, additional, Dorooki, Kiyana, additional, Sabouni, Roghayeh Sabouhi, additional, moghadam, Mahla barati, additional, Shafaei, Negin, additional, Karimi, Ehsan, additional, and Oskoueian, Ehsan, additional

Published
2024
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