id:"swepub:oai:DiVA.org:kth-341596"
Sökning: id:"swepub:oai:DiVA.org:kth-341596" > Predicting moisture...
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- Alzweighi, MossabKTH,Hållfasthetslära (författare)
Predicting moisture penetration dynamics in paper with machine learning approach
- Artikel/kapitelEngelska2024
Förlag, utgivningsår, omfång ...
- Elsevier BV,2024
- printrdacarrier
Nummerbeteckningar
- LIBRIS-ID:oai:DiVA.org:kth-341596
- https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-341596URI
- https://doi.org/10.1016/j.ijsolstr.2023.112602DOI
Kompletterande språkuppgifter
- Språk:engelska
- Sammanfattning på:engelska
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- SwePubSwePub
Klassifikation
Anmärkningar
- QC 20231227
- In this work, we predicted the gradient of the deformational moisture dynamics in a sized commercial paper by observing the curl deformation in response to the one-sided water application. The deformational moisture is a part of the applied liquid which ends up in the fibers causing swelling and subsequent mechanical response of the entire fiber network structure. The adapted approach combines traditional experimental procedures, advanced machine learning techniques and continuum modeling to provide insights into the complex phenomenon relevant to ink-jet digital printing in which the sized and coated paper is often used, meaning that not all the applied moisture will reach the fibers. Key material properties including elasticity, plastic parameters, viscoelasticity, creep, moisture dependent behavior, along with hygroexpansion coefficients are identified through extensive testing, providing vital data for subsequent simulation using a continuum model. Two machine learning models, a Feedforward Neural Network (FNN) and a Recurrent Neural Network (RNN), are probed in this study. Both models are trained using exclusively numerically generated moisture profile histories, showcasing the value of such data in contexts where experimental data acquisition is challenging. These two models are subsequently utilized to predict moisture profile history based on curl experimental measurements, with the RNN demonstrating superior accuracy due to its ability to account for temporal dependencies. The predicted moisture profiles are used as inputs for the continuum model to simulate the associated curl response comparing it to the experiment representing “never seen” data. The result of comparison shows highly predictive capability of the RNN. This study melds traditional experimental methods and innovative machine learning techniques, providing a robust technique for predicting moisture gradient dynamics that can be used for both optimizing the ink solution and paper structure to achieve desirable printing quality with lowest curl propensities during printing.
Ämnesord och genrebeteckningar
- TEKNIK OCH TEKNOLOGIER Maskinteknik Teknisk mekanik hsv//swe
- ENGINEERING AND TECHNOLOGY Mechanical Engineering Applied Mechanics hsv//eng
- Curl Deformation
- Feedforward Neural Network
- Machine Learning
- Moisture Penetration Dynamics
- Paper Materials
- Recurrent Neural Network
Biuppslag (personer, institutioner, konferenser, titlar ...)
- Mansour, RamiKTH,Hållfasthetslära,Department of Mechanical and Production Engineering, Aarhus University, 8200 Aarhus N, Denmark(Swepub:kth)u1wleudn (författare)
- Maass, AlexanderInstitute of Bioproducts and Paper Technology, Graz University of Technology, Inffeldgasse 23, 8010 Graz, Austria, Inffeldgasse 23; CD Laboratory for Fiber Swelling and Paper Performance, 8010 Graz, Austria (författare)
- Hirn, UlrichInstitute of Bioproducts and Paper Technology, Graz University of Technology, Inffeldgasse 23, 8010 Graz, Austria, Inffeldgasse 23; CD Laboratory for Fiber Swelling and Paper Performance, 8010 Graz, Austria (författare)
- Kulachenko, Artem,1978-KTH,Hållfasthetslära,CD Laboratory for Fiber Swelling and Paper Performance, 8010 Graz, Austria(Swepub:kth)u1at8fc7 (författare)
- KTHHållfasthetslära (creator_code:org_t)
Sammanhörande titlar
- Ingår i:International Journal of Solids and Structures: Elsevier BV288, s. 112602-0020-76831879-2146
Internetlänk
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- International Journal of Solids and Structures (Sök värdpublikationen i LIBRIS)
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