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+# Damage Classification in Dual Phase Steels
+
+This app is a web-based application for damage classification in dual phase steels using the [gradio](https://www.gradio.app/) web development framework.
+
+Input to the app is a (panoramic) image of a dual-phase steel specimen taken in an electron microscope (SEM).
+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.
+
+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.
+
+After the inclusions are identified, a second dedicated neural network identifies the following damage classes:
+- Martensite Cracking
+- Notch Effect
+- Interface Decohesion.
+Further details about the definition of the damage classes can be found in the references below.
+Additionally, shadows cast by the electron beam during image acquisition are also identified as an imaging artefact.
+
+
+
+## References
+
+Please cite the following works if you use this app.
+
+- 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)
+
+- 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)
+
+- original data and code, including the network weights,  can be found at Zenodo [link](https://zenodo.org/records/8065752)
+
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 title: DP800 DamageClassification
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