Preferable Workspace for Fatigue Life Improvement of Flexible-Joint Robots Under Percussive Riveting
Bibliographic record
Abstract
This paper proposes a method to find the preferable workspace for fatigue life improvement of robots with flexible joints under percussive riveting. The development is motivated by the growing interest in using industrial robots to replace human operators for percussive riveting operations in aerospace assembly. A most important characteristic of robotic percussive riveting is the repetitive impacts generated by the percussive rivet gun. These impacts induce forced vibrations to the robot, and the joint shaft fatigue due to the resulting stress cycles must be prevented. This paper aims at finding the preferable workspace for fatigue life improvement of the robot, that is, the end-effector positions where the joint stresses are below the endurance limit. For this purpose, a structural dynamic model is established for the robot under percussive riveting. Then, an approximate analytical solution is formulated for the torsional stresses of the robot joints. Once the distributions of the stresses are obtained over the workspace, the preferable workspace for fatigue life improvement can be found by comparing the stresses with the endurance limit. Simulation studies are carried out for a mobile robot under percussive riveting. It is found that the dynamic response of the robot to the percussive riveting varies dramatically over the workspace. The method is then used to obtain the preferable positions of the robot end-effector for fatigue resistance.
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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 | 0.002 | 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".