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A Novel Multi-Region, Multi-Phase, Multi-Component-Mixture Modeling Approach to Predicting the Thermodynamic Behaviour of Thermoplastic Composites during the Consolidation Process
In the processing of thermoplastic composites, great importance is attributed to the consolidation step, as it can significantly reduce the porosity of the semi-finished product and thus influence considerably the quality of the final component. This work presents an approach to modeling the thermod...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9658174/ https://www.ncbi.nlm.nih.gov/pubmed/36365774 http://dx.doi.org/10.3390/polym14214785 |
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author | Kobler, Eva Birtha, Janos Marschik, Christian Straka, Klaus Steinbichler, Georg Zwicklhuber, Paul Schlecht, Sven |
author_facet | Kobler, Eva Birtha, Janos Marschik, Christian Straka, Klaus Steinbichler, Georg Zwicklhuber, Paul Schlecht, Sven |
author_sort | Kobler, Eva |
collection | PubMed |
description | In the processing of thermoplastic composites, great importance is attributed to the consolidation step, as it can significantly reduce the porosity of the semi-finished product and thus influence considerably the quality of the final component. This work presents an approach to modeling the thermodynamic behavior of composite materials during hot-press consolidation. For this purpose a multi-region, multi-phase and multi-component-mixture model was developed using the simulation toolbox OpenFOAM(®). The sensitivity of the model was tested by varying the thermal parameters and mesh resolution, confirming its robustness. Validity of the model was confirmed by comparing simulation results to experimental data for (i) polycarbonate with 44% carbon fiber by volume and (ii) polypropylene with 45.3% glass fiber by volume. The simulation allows very precise estimation of when a particular temperature, such as the glass transition temperature or melting point, will be reached at the core of a composite. In relation to the total process time, maximum deviation of the simulation from the experimental data amounted to 2.84%. Therefore, the model is well suited for process optimization, it offers a basis for further model implementations and the creation of a digital twin. |
format | Online Article Text |
id | pubmed-9658174 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96581742022-11-15 A Novel Multi-Region, Multi-Phase, Multi-Component-Mixture Modeling Approach to Predicting the Thermodynamic Behaviour of Thermoplastic Composites during the Consolidation Process Kobler, Eva Birtha, Janos Marschik, Christian Straka, Klaus Steinbichler, Georg Zwicklhuber, Paul Schlecht, Sven Polymers (Basel) Article In the processing of thermoplastic composites, great importance is attributed to the consolidation step, as it can significantly reduce the porosity of the semi-finished product and thus influence considerably the quality of the final component. This work presents an approach to modeling the thermodynamic behavior of composite materials during hot-press consolidation. For this purpose a multi-region, multi-phase and multi-component-mixture model was developed using the simulation toolbox OpenFOAM(®). The sensitivity of the model was tested by varying the thermal parameters and mesh resolution, confirming its robustness. Validity of the model was confirmed by comparing simulation results to experimental data for (i) polycarbonate with 44% carbon fiber by volume and (ii) polypropylene with 45.3% glass fiber by volume. The simulation allows very precise estimation of when a particular temperature, such as the glass transition temperature or melting point, will be reached at the core of a composite. In relation to the total process time, maximum deviation of the simulation from the experimental data amounted to 2.84%. Therefore, the model is well suited for process optimization, it offers a basis for further model implementations and the creation of a digital twin. MDPI 2022-11-07 /pmc/articles/PMC9658174/ /pubmed/36365774 http://dx.doi.org/10.3390/polym14214785 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kobler, Eva Birtha, Janos Marschik, Christian Straka, Klaus Steinbichler, Georg Zwicklhuber, Paul Schlecht, Sven A Novel Multi-Region, Multi-Phase, Multi-Component-Mixture Modeling Approach to Predicting the Thermodynamic Behaviour of Thermoplastic Composites during the Consolidation Process |
title | A Novel Multi-Region, Multi-Phase, Multi-Component-Mixture Modeling Approach to Predicting the Thermodynamic Behaviour of Thermoplastic Composites during the Consolidation Process |
title_full | A Novel Multi-Region, Multi-Phase, Multi-Component-Mixture Modeling Approach to Predicting the Thermodynamic Behaviour of Thermoplastic Composites during the Consolidation Process |
title_fullStr | A Novel Multi-Region, Multi-Phase, Multi-Component-Mixture Modeling Approach to Predicting the Thermodynamic Behaviour of Thermoplastic Composites during the Consolidation Process |
title_full_unstemmed | A Novel Multi-Region, Multi-Phase, Multi-Component-Mixture Modeling Approach to Predicting the Thermodynamic Behaviour of Thermoplastic Composites during the Consolidation Process |
title_short | A Novel Multi-Region, Multi-Phase, Multi-Component-Mixture Modeling Approach to Predicting the Thermodynamic Behaviour of Thermoplastic Composites during the Consolidation Process |
title_sort | novel multi-region, multi-phase, multi-component-mixture modeling approach to predicting the thermodynamic behaviour of thermoplastic composites during the consolidation process |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9658174/ https://www.ncbi.nlm.nih.gov/pubmed/36365774 http://dx.doi.org/10.3390/polym14214785 |
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