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--- |
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title: DP800 DamageClassification |
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emoji: 🐠 |
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colorFrom: gray |
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colorTo: yellow |
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sdk: gradio |
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sdk_version: 4.22.0 |
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app_file: app.py |
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pinned: false |
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license: cc-by-nc-nd-4.0 |
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--- |
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# Damage Classification in Dual Phase Steels |
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This app is a web-based application for damage classification in dual phase steels using the [gradio](https://www.gradio.app/) web development framework. |
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Input to the app is a (panoramic) image of a dual-phase steel specimen taken in an electron microscope (SEM). |
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In a first step, potential damage sites are identified using a clustering approach: Damage sites typically appear as small holes in the surface of the sample and are, therefore, not easily visible in the image due to the three-dimensional structure of the sample surface when the electron beam hits the sample. |
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Then, inclusions are identified using a dedicated neural network. Inclusions originate from foreign particles that are embedded into the sample matrix during the manufacturing process and are typically much bigger than other types of damage. |
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After the inclusions are identified, a second dedicated neural network identifies the following damage classes: |
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- Martensite Cracking |
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- Notch Effect |
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- Interface Decohesion. |
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Further details about the definition of the damage classes can be found in the references below. |
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Additionally, shadows cast by the electron beam during image acquisition are also identified as an imaging artefact. |
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## References |
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Please cite the following works if you use this app. |
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- Kusche C, Reclik T, Freund M, Al-Samman T, Kerzel U, Korte-Kerzel S (2019) Large-area, high-resolution characterisation and classification of damage mechanisms in dual-phase steel using deep learning. PLoS ONE 14(5): e0216493. [link](https://doi.org/10.1371/journal.pone.0216493) |
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- Setareh Medghalchi, Ehsan Karimi, Sang-Hyeok Lee, Benjamin Berkels, Ulrich Kerzel, Sandra Korte-Kerzel, Three-dimensional characterisation of deformation-induced damage in dual phase steel using deep learning, Materials & Design, Volume 232, 2023, 112108, ISSN 0264-1275, [link] (https://doi.org/10.1016/j.matdes.2023.112108) |
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- original data and code, including the network weights, can be found at Zenodo [link](https://zenodo.org/records/8065752) |
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
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