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An image-based approach for structure investigation and 3D numerical modelling of polymeric foams

TitleAn image-based approach for structure investigation and 3D numerical modelling of polymeric foams
Publication TypeArticolo su Rivista peer-reviewed
Year of Publication2021
AuthorsTagliabue, S., Andena L., Nacucchi Michele, and De Pascalis F.
JournalJournal of Polymer Research
Volume28
Keywords3D modeling, 3D numerical modelling, Autocorrelation functions, Computerized tomography, Engineering applications, Finite element meshes, Mean intercept length, Mechanical simulations, Microstructure, Microstructure characterisation, Morphology, Physical and mechanical properties, Polymers
Abstract

Polymeric expanded materials are of great importance in many engineering applications. Despite this, as of today the development of models able to describe the mechanical behaviour of these material as a function of their microstructure is still an open challenge. In this study an image-based approach is proposed for both microstructure characterisation and 3D numerical mechanical simulations. Microstructure is investigated through different algorithms, such as Mean Intercept Length and Autocorrelation function, to determine synthetic parameters able to describe the internal structure. A novel algorithm has been developed to convert the images obtained from computed tomography into a finite element mesh with an optimized number of elements: this method preserves the original structure and can also be used to generate other fictitious structures that can be analysed. The investigation led to the identification of general relationships between foam microstructure and relevant macroscopic physical and mechanical properties. These relationships can serve as a tool to optimize foam morphology or product final properties for several different engineering applications. © 2021, The Polymer Society, Taipei.

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URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85100477475&doi=10.1007%2fs10965-021-02438-9&partnerID=40&md5=7212295ceb9cc7ae08519af2a81ab8c1
DOI10.1007/s10965-021-02438-9
Citation KeyTagliabue2021