Multiscale Modelling and Experimental Characterization of Additively Manufactured Polymer and Polymer Nanocomposites
Notice bibliographique
Résumé
Fused deposition modelling (FDM) is a widely utilized additive manufacturing process that is used in several industrial sectors. The advantages of additive manufacturing over conventional methods are the ability to rapidly produce intricate forms and sizes with precision, without the need for additional machining to get a finished product or the assembly of many pieces to create a single component. The deposition process based on the layer-by-layer principle results in the formation of intrinsic voids inside the bulk of printed parts. The presence of voids, in conjunction with the various printing parameters employed throughout the process, has a significant impact on the performance in relation to their electrical, mechanical, or other properties. Researchers have employed experimental characterisation methods to comprehend the impact of these parameters and the concentration of voids on the properties of printed parts. Because of these voids and parameters effect, additively manufactured parts exhibit anisotropic behavior due to their inherent microstructure characteristics, which cannot be accurately captured by macroscale computational models. When employing nanofillers to enhance the characteristics of printed components, it is imperative for the computational tool to accurately account for the dispersion and agglomeration phenomena occurring at a smaller scale. The current models are unable to adequately address this. In terms of the experimental characterization, the manufacturing method of feedstock input filament necessitates over 24 hours for the solvent employed in the procedure to undergo evaporation. Furthermore, the dispersion observed in the literature at lower concentrations of nanofillers is reported poor in experimental studies. Therefore, to answer these challenges, this thesis presents a hierarchical multiscale model for studying the effect of fused deposition modelling process parameters on mechanical properties. This study examines four parameters: raster orientation, layer height, build orientation, and porosity. The generation of representative volume elements (RVEs) involves the mimicking of the microstructure of the printed parts. Once the understanding of effect of parameters concluded, the thesis introduced a novel geometrical void model integrated with asymptotic homogenization to study first time the impact of inter-raster distance, bead width, and layer height on the concentration of voids (in terms of volume) at constant infill density. The next study focussed on improvement of additively manufactured parts properties by incorporating multiwalled carbon nanotubes (MWCNTs) as nanofillers in polymer matrix. A novel computational model with three scales to determine the mechanical characteristics of nanocomposites fabricated using 3D printing is proposed. The proposed computational framework incorporates the Halpin Tsai and Eshelby Mori Tanaka models at the microscale integrated with the asymptotic homogenization model at the meso and macro scales. The work further presents a unique experimental approach for water-quenching, which is used to prepare insitu nanocomposite sheets while producing nanocomposite filaments. In addition to tensile testing, a morphological analysis was undertaken to examine the dispersion and agglomeration impact and efficacy of the newly proposed methodology. The present research work contributes to the scientific community as new effective modelling methods and novel experimental characterization method to study and characterize the polymer and polymer nanocomposites 3D printed parts. The polymer and nanocomposite computational models effectively provide tools for designing the additively manufactured parts with improved properties. The experimental method enables research community with rapidity in nanocomposite filament manufacturing having good dispersion and increased properties. The present study will allow new development in additively manufacturing industries such as cranioplasty biomedical industry, with reduced experimental testing, improved properties by controlling the parameters of FDM method.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».