Modelling and Validation of a Cable-Assisted Robotic System for Machining Application
Bibliographic record
Abstract
In the rapidly evolving landscape of manufacturing processes, robotic systems have gained prominence for their precision and efficiency. A cable-assisted robotic system (CARS) tailored for machining operations, offering enhanced stiffness is presented in this thesis. This thesis aims to understand CARS dynamic behaviour by experimentally examining the system under various conditions. These conditions include equilibrium and imbalance states, varying the robot postures, and varying the pulley locations. Understanding the influence of these variables aids in determining the feasibility of such a system. Additionally, the influence of the cables on the systems is examined. Cable types are varied from solid to stranded, as well as the number of cables and the size of the cables. The cable tension is also manipulated and the effects are investigated. The cables act as massless redundant links providing the robot with additional stiffness, however the extent to which additional stiffness is applied needs to be determined. CARS is also examined in a quasi-static state. The quasi-static state differs from the static, impacting the stability limits at the low frequencies and giving a better understanding of the dynamics of the system for machining applications. Finally, CARS is tested in machining by performing chatter tests under the use of different cables. Furthermore, this thesis presents a novel mathematical model for CARS, consisting of a semi-empirical cable model integrated with a serial robot model. The cable properties are identified experimentally, while the robot properties are determined using optimization techniques. The model is derived using the Euler-Lagrange method, and is validated by simulating CARS response and comparing it to the results obtained through experimentation. Finally, the model is optimized using a genetic algorithm to determine the cable configuration that maximizes the dynamic stiffness. These findings have implications for robotic machining applications and broader industrial robotics.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".