Quantitative comparison of cable models through dimensional analysis
Bibliographic record
Abstract
The use of cables enables the design of Cable-Driven Parallel Robots (CDPRs) of various sizes and payloads. However, this scalability does not always extend to their mechanical models: different sizes and payloads generally require different models. Unlike standard robots, CDPR cables are prone to sag and stretch. These phenomena are well documented, and several models have been proposed to describe them. Despite this, there is no established method for selecting the most appropriate model for a given application. This article addresses this issue by proposing a quantitative comparison using dimensional analysis applied to four common cable models: Massless Rigid, Massless Elastic, Catenary, and Catenary Elastic. The method compares cable-end positions under identical loading conditions for each model. A simulation study compares computed and predicted data, showing that the relative error of a model can be predicted with this approach. A large-scale CDPR is then used for experimental validation. The proposed methodology and results suggest that dimensional analysis is a valid tool for selecting the appropriate cable model. • Novel method using dimensional analysis to find cable-end positional error. • Implementation uses massless rigid cable model, simplifying and speeding setup. • Applicable across diverse Cable-Driven Parallel Robot (CDPR) designs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".