Python code: Classification of in situ high speed videos of the gravure printing fluid splitting process using deep learning
Date
2022
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.
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 (DOI: 10.25534/tudatalib-191.2). 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".
Further information can be found in the README-file and in the publication:
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. DOI: 10.1007/s11998-022-00687-x
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 (DOI: 10.25534/tudatalib-191.2). 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".
Further information can be found in the README-file and in the publication:
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. DOI: 10.1007/s11998-022-00687-x
Subject
gravure printing;fluid splitting;Python code;deep learning;video classification;pattern formation;machine learning;viscous fingeringDFG subject classification
4.21-03 Mechanische Verfahrenstechnik4.22-03 Strömungsmechanik
4.31-03 Beschichtungs- und Oberflächentechnik
URI
https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3552https://doi.org/10.48328/tudatalib-938
Related third party funded projects
DFG | SFB1194 | TP C01 DörsamRelated Resources
- Is cited by: DOI:10.1007/s11998-022-00687-x
- Cites: DOI:10.25534/tudatalib-191.2
- Cites: ISBN:978-1-83864-483-3
- Cites: https://github.com/PacktPublishing/PyTorch-Computer-Vision-Cookbook
- Cites: https://github.com/ladisk/pyMRAW
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Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial 4.0