Cartesian Elastodynamics Modelling of Parallel-kinematics Machines Under High-frequency, Small-amplitude Manoeuvres
Bibliographic record
Abstract
A novel class of three-limb, full-mobility parellel-kinematics machines (PKMs), dubbed the SDelta, is proposed as a promising alternative to the traditional six-limb Stewart-Gough platforms.This simple architecture, with fewer moving components, leads to a lower inertia load, which extends its applications domain, the SDelta being deemed fit for generating high-frequency, small-amplitude (HFSA) motions, which are needed, e.g., in the inertia-parameter identification of rigid bodies.Prior work conducted by the applicant on three-limb, full-mobility PKMs includes architecture design plus kinematics, singularity and dexterity analyses.This work was conducted upon modelling the PKM as a multi-rigid-body system.However, for HFSA applications, where high speeds are required, the inherent flexibility of the light limb rods should be taken into account.Thus, the PKM should be modeled as a multibody system with rigid and flexible links.In this vein, a concise lumped-parameter elastodynamics linear model is essential, since it includes the system stiffness and vibration characteristics in a swift, effective way.Instead of a detailed n-degree-of-freedom(n-dof) generalized model considering flexibility and inertia of all system links, this thesis focuses on the six-dof simplified model in Cartesian space.This model is deemed suitable for flexible mechanical systems whose operation link is much stiffer and heavier than its counterparts coupling it to the rigid base.In this case, the system elastodynamics model can be simplified into a rigid moving platform (MP) mounted on a massless, linearly elastic suspension.Under this assumption, the system inertia is lumped into the rigid MP, while the system stiffness is lumped into a Cartesian spring.The whole system is thus simplified into a Cartesian mass-spring model.This model is not only a natural extension of its one-dof mass-spring counterpart, but also a pertinent simplification of the n-dof generalized model.Our model is deemed to be a convenient and useful tool in the preliminary-design stages, in which the detailed di-Firstly, I would like to express my most sincere gratitude to my supervisors, Profs.Jorge Angeles and James Forbes.Starting as a student with little research experience, I faced numerous challenges during my PhD studies, which I could never have overcome without their unreserved guidance and encouragement.I would like to thank them for introducing me to the exciting world of scientific research, which led to my determination of embarking on this path.Moreover, I would like to express my gratitude to Profs.Meyer Nahon and Arun K. Misra
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".