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Enregistrement W6996513830

Static and vibration analysis of composite structures for robotic application

2022· dissertation· en· W6996513830 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity Library (University of Saskatchewan) · 2022
Typedissertation
Langueen
DomaineEngineering
ThématiqueComposite Structure Analysis and Optimization
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésFibre-reinforced plasticComposite numberVibrationBeam (structure)Finite element methodGlass fiberDeformation (meteorology)IsotropyFiber
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

During the past few decades, notable advances have been made in the area of polymer matrix composite materials and their use in structures and mechanisms has markedly increased. Composite fiber reinforced polymer (FRP) materials have been used to build carbon fiber reinforced polymer (CFRP) and glass fiber reinforced polymer (GFRP) beams. In this thesis, the behaviour of CFRP and GFRP beams and the parameters that impact their static and free vibration response were investigated. Also, the use and effectiveness of these beams to replace aluminum alloys (AA) and steel beams in robot structures were examined.\nFrom a structural analysis viewpoint, the design and analysis of composite materials (and members and structures that are constructed using these materials) is more challenging than structures constructed using conventional isotropic materials such as steel and AA. In this research, design charts were developed and a simplified approach for selection of parameters that govern the behaviour of CFRP and GFRP beams is presented. Fiber angle orientation, laminate thickness, materials of construction, cross-sectional shape, and density were the main parameters that were considered. These parameters were studied as they have an impact on the structural response (i.e., deformation patterns, deflections, natural frequencies, strength, forced vibration response) and mass of the FRP beams.\nAs the selection of the design parameters depends on the mode of loading, design charts were developed for axial, bending, torsional, and combined bending-torsional loading conditions. The CFRP and GFRP beams were analyzed using detailed three-dimensional (3D) finite element (FE) analyses and closed-form analytical solutions available in the literature. By comparing the numerical and analytical solutions, the FE models and results were validated. The results showed that the design charts and simplified approach can be used to determine the fiber angle orientation, laminate thickness, cross-sectional shape, and materials that could provide the desired static and free vibration responses.\nTo examine the effectiveness of FRP beams to improve static and free vibration performance of a robot manipulator, a detailed simulation study using FE analysis was also carried out. For this purpose, a five degree of freedom robot manipulator previously developed in the Robotics Laboratory at the University of Saskatchewan was considered. This robot was constructed using\nAA and steel materials. The FE simulations performed in this study focused on determining stiffness and strength (while considering the mass) of the robot arm with CFRP beams and comparing the structural performance with the AA manipulator. Using the developed FE models, the CFRP arm deflections, natural frequencies, and safety factors for strength were determined. The FE analyses results were verified by comparing to closed-form analytical solutions and, where possible, validated by comparing to experimental results available in the literature. The obtained results showed that the CFRP arm has a higher specific strength (i.e., payload to weight ratio), higher stiffness and natural frequency, and lower deflections compared with the AA arm. Additionally, the CFRP robotic arm was lighter than the AA arm. Lighter robot structures are advantageous as they require smaller motors and actuators with lower power consumption and hence improve energy efficiency.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
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 enseignants0,156
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

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

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 Opus0,003
Tête enseignante GPT0,164
Écart entre enseignants0,161 · 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 tête enseignante, pas un consensus.

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
Publié2022
Routes d'admission1
Résumé présentoui

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