A Framework on Robotic Percussive Riveting for Aircraft Assembly Automation
Bibliographic record
Abstract
Presented in this paper is a framework for the implementation of a robotic percussive riveting system, a new robot application for aircraft assembly. It is shown here that a successful robot application to the automation of a process requires in-depth research of the process and the interaction with the robot. For this purpose, a process planning-driven approach is proposed to guide a robot application research. A typical process planning will involve a list of key considerations including: process sequence, process parameters, process tooling, and process control. Through this list, a number of key research issues are identified for robotic percussive riveting, such as rivet pattern planning, riveting time determination, riveting tooling design and rivet insertion control. The detailed research on these issues has effectively created know-how for the successful implementation of our robotic percussive riveting system. The research results presented in this paper have been applied to implement our robotic percussive riveting system. The physical system involving three controllers for three subsystems, one for the robot, one for the tooling and one for the bucking bar gantry. The control sequence has been successfully tested and implemented to perform percussive riveting on sheet metal panels and composite panels.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".