Dynamic Analysis for Robotic Integration of Tooling Systems
Bibliographic record
Abstract
This paper presents a dynamic analysis method for robotic integration of tooling systems. This development is motivated by the fact that many modern robotic automation tasks require large and heavy tooling systems. Yet, the integration of these tooling systems is usually done only considering the geometric constraints and weights without resorting to dynamic analysis. To resolve this problem, the equations of motion of a robot with inclusion of a tooling system are derived using the Lagrangian formulation. Three performance indices are introduced to evaluate the influence of the tooling system on the overall dynamics. The first index measures the energy consumption due to the tooling system’s motion, the second index evaluates the influence of the tooling system on the fundamental frequency, and the third one is the dynamic manipulability ellipsoid to measure the acceleration capability of the tool tip. Simulation studies are carried out to provide guidelines for the design of tooling systems. To demonstrate its effectiveness, the proposed method is applied to facilitate the tooling integration used in the robotic riveting for aerospace assembly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".