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Record W6996513830

Static and vibration analysis of composite structures for robotic application

2022· dissertation· en· W6996513830 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2022
Typedissertation
Languageen
FieldEngineering
TopicComposite Structure Analysis and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsFibre-reinforced plasticComposite numberVibrationBeam (structure)Finite element methodGlass fiberDeformation (meteorology)IsotropyFiber
DOInot available

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.003
GPT teacher head0.164
Teacher spread0.161 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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