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Enregistrement W2592271748

INJECTION MOLDING OF FLAX FIBER BIOCOMPOSITES BY SIMULATION AND OPTIMIZATION

2017· dissertation· en· W2592271748 sur OpenAlexfundno aff
Dong Qing He

Notice bibliographique

RevueUniversity Library - University of Saskatchewan (University of Saskatchewan) · 2017
Typedissertation
Langueen
DomaineEngineering
ThématiqueInnovations in Concrete and Construction Materials
Établissements canadiensnon disponible
Organismes subventionnairesNatural Sciences and Engineering Research Council of Canada
Mots-clésMolding (decorative)Materials scienceFiberComposite materialBiocompositePolymer scienceComposite number
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Flax (Linum usitatissimum) fibers have the advantages of low density, low cost, and recyclability and are considered as a potential material to reinforce plastic materials. Though Canada is one of the largest seed flax growing countries in the world, the utilization of flax fibers as reinforcement in composites is not as developed as in Europe. Indeed, in Canada, a large amount of flax straws are left in the fields and burned by farmers each year. Therefore, development of technologies to make use of flax straws for reinforcement in composites and for other purposes has huge benefits to both the material industries and flax farmers in Canada. This thesis presented a study of flax fibers reinforced biocomposites by injection molding through modeling and optimization. The focus of the study was to understand the relationships between the properties of biocomposites and the processing conditions through the experiment and improve the qualities of biocomposites by optimizing the processing conditions. In this thesis, biocomposites were successfully produced by injection molding with a proposed processing scheme. The influence of flax fiber loading and processing conditions, including injection temperature and pressure on the mechanical properties (tensile properties and flexural properties), and water absorption of biocomposites was investigated. The study also experimentally investigated the effect of the processing conditions (fiber content and temperature) on the rheological properties of biocomposites. In order to implement the simulation analysis of injection molding for biocomposites, the Cross-WLF model was employed to obtain the rheological information of biocomposites. Further, a systematic approach on simulation analysis and optimization of injection molding was proposed to minimize the shrinkage and warpage of biocomposites. Several conclusions are drawn from this study: 1) With respect of the influence of the processing conditions on the properties of biocomposites, (a) Fiber content is the most significant impact factor influencing the mechanical properties of biocomposites compared with the other two processing conditions and the tensile properties and flexural properties of biocomposites dreamingly increased with flax fiber content; (b) lower injection temperature led to higher tensile properties and flexural properties; (c) Water absorption of biocomposites was significantly dependent on fiber content and injection temperature; (d) Injection pressure had no significant effect on either mechanical properties or water absorption. 2) In the study on the rheological characteristics, (a) The shear viscosity of biocomposites increased with fiber content, but at very high shear rates (from 5,000 to 10,000 S−1), the shear viscosities of biocomposites with various fiber content (from 0 to 30%) tended to be the same; (b) The shear viscosity of biocomposites decreased with temperature, and at higher shear rate, all the shear viscosity variations as function of shear rates followed the same rate for different temperatures; (c) At high shear rate, the shear viscosity mostly depended on the shear rate rather than fiber content and temperature; (d) A method was presented to determine the seven parameters of the Cross-WLF model for biocomposites. 3) For minimizing the shrinkage and warpage of injection molded biocomposites, (a) The significant factors on the shrinkage and warpage of biocomposites by injection molding were injection temperature, packing time, and packing pressure; (b) The optimization of the injection molding of biocomposites for reducing the shrinkage and warpage of biocomposites was successful by integrating design of experiment (DOE) and simulation technique. The contribution of this thesis includes: 1) In the field of biocomposites reinforcement, the study has shown a great promise to use flax fibers to enhance the mechanical properties of thermoplastics, in particular an increase of 41.83% in tensile strength and an increase of 47.13% in flexural strength. In addition, this work has provided a mathematical relationship between the processing condition of injection molding and the mechanical properties of biocomposites, which would be important to control the manufacturing process to reach desired mechanical properties. 2) In the field of optimal design and manufacturing of flax fiber biocomposites, this work has provided: (a) an effective method to determine the parameters in the rheology model of the biocomposites melt, which has been an important step in simulating the process, and this method has a generalized implication to other types of biocomposites; and (b) a systematic approach to optimize the injection molding process for minimizing the shrinkage and warpage of biocomposites, which are the two most important quality issues in biocomposites.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,013

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,006
Tête enseignante GPT0,175
Écart entre enseignants0,170 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentnon

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