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Viscoelastic model with complex rheological behavior (VisCoR): incremental formulation
RISE Research Institutes of Sweden, Materials and Production, Polymeric Materials and Composites.
RISE Research Institutes of Sweden, Materials and Production.
Luleå University of Technology, Sweden.
2020 (English)In: Advanced Manufacturing: Polymer and Composites Science, ISSN 2055-0359Article in journal (Refereed) Epub ahead of print
Abstract [en]

A thermo-rheologically complex linear viscoelastic material model, accounting for temperature and degree of cure (DoC), is developed starting with series expansion of the Helmholtz free energy and systematically implementing simplifying assumptions regarding the material behavior. In addition to the temperature and DoC dependent shift factor present in rheologically simple models, the derived novel model contains three cure and temperature dependent functions. The first function is identified as the rubbery modulus. The second is a weight factor to the transient integral term in the model and reflects the current temperature and cure state, whereas the third function is under the sign of the convolution integral, thus affecting the “memory” of the material. An incremental form of this model is presented which, due to improved approximation inside the time increment, has better numerical convergence than most of the similar forms. Parametric analysis is performed simulating stress development in a polymer, geometrically constrained in the mold during curing and cool-down. The importance of using proper viscoelastic model is shown, and the role of parameters in the model is revealed and discussed. © 2019, © 2019 The Author(s).

Place, publisher, year, edition, pages
Taylor and Francis Ltd. , 2020.
Keywords [en]
Process modeling, shift factors, thermo-rheological complexity, viscoelasticity
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-43371DOI: 10.1080/20550340.2019.1709010Scopus ID: 2-s2.0-85077871457OAI: oai:DiVA.org:ri-43371DiVA, id: diva2:1389375
Note

Funding details: 2017-04873; Funding details: 821019; Funding text 1: The authors would like to thank the Swedish Innovation program VINNOVA (under grant agreement number 2017-04873) and the European Union’s Horizon2020 research program (under grant agreement number 821019) for funding this study.

Available from: 2020-01-29 Created: 2020-01-29 Last updated: 2020-01-29Bibliographically approved

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