TUdatalib : Python code: Classification of in situ high speed videos of the gravure printing fluid splitting process using deep learning

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dc.contributor.author	Brumm, Pauline
dc.contributor.author	Ciotta, Nicola
dc.contributor.author	Sauer, Hans Martin
dc.contributor.author	Blaeser, Andreas
dc.contributor.author	Doersam, Edgar
dc.date.accessioned	2022-08-26T23:12:57Z
dc.date.available	2022-08-26T23:12:57Z
dc.date.issued	2022
dc.identifier.uri	https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3552
dc.identifier.uri	https://doi.org/10.48328/tudatalib-938
dc.description	The files that are made available here are all the key components that are needed to recreate the automated classification of high speed videos of the gravure printing fluid splitting process using deep learning. The purpose of this was to create a tool that can distinguish the distinct regimes of pattern formation. This could ultimately lead to a better understanding of the fluid splitting process and a complete map of pattern formation regimes. <br /> <br /> The zip-file "Python_code" contains the Python script that was used to extract PNG frames from a video dataset created by Julian Schäfer in 2019 (<a href="https://doi.org/10.25534/tudatalib-191.2">DOI: 10.25534/tudatalib-191.2</a>). The result of this frame extraction is provided as "Frame_dataset_PNG_8_bit.zip". Additionally, "Python_code.zip" contains the scripts for training the deep learning models and using them for inference. All trained models for the associated publication can be found in the zip-file "models".<br /> <br /> Further information can be found in the README-file and in the publication:<br /> Pauline Brumm, Nicola Ciotta, Hans Martin Sauer, Andreas Blaeser, Edgar Dörsam, 2022. Deep learning study of induced stochastic pattern formation in the gravure printing fluid splitting process, Journal of Coatings Technology and Research. <a href="https://doi.org/10.1007/s11998-022-00687-x">DOI: 10.1007/s11998-022-00687-x</a>	de_DE
dc.language.iso	en	de_DE
dc.relation	IsCitedBy;DOI;10.1007/s11998-022-00687-x
dc.relation	Cites;DOI;10.25534/tudatalib-191.2
dc.relation	Cites;ISBN;978-1-83864-483-3
dc.relation	Cites;URL;https://github.com/PacktPublishing/PyTorch-Computer-Vision-Cookbook
dc.relation	Cites;URL;https://github.com/ladisk/pyMRAW
dc.rights	Creative Commons Attribution-NonCommercial 4.0
dc.rights.uri	https://creativecommons.org/licenses/by-nc/4.0/
dc.subject	gravure printing	de_DE
dc.subject	fluid splitting	de_DE
dc.subject	Python code	de_DE
dc.subject	deep learning	de_DE
dc.subject	video classification	de_DE
dc.subject	pattern formation	de_DE
dc.subject	machine learning	de_DE
dc.subject	viscous fingering	de_DE
dc.subject.classification	4.21-03 Mechanische Verfahrenstechnik	de_DE
dc.subject.classification	4.22-03 Strömungsmechanik	de_DE
dc.subject.classification	4.31-03 Beschichtungs- und Oberflächentechnik	de_DE
dc.subject.ddc	660
dc.subject.ddc	620
dc.title	Python code: Classification of in situ high speed videos of the gravure printing fluid splitting process using deep learning	de_DE
dc.type	Text	de_DE
dc.type	Software	de_DE
dc.type	Image	de_DE
dc.type	Model	de_DE
tud.project	DFG \| SFB1194 \| TP C01 Dörsam	de_DE
tud.unit	TUDa
tud.history.classification	Version=2020-2024;403-03 Mechanische Verfahrenstechnik
tud.history.classification	Version=2020-2024;404-03 Strömungsmechanik
tud.history.classification	Version=2020-2024;405-03 Beschichtungs- und Oberflächentechnik