Tool Accessibility Analysis for Robotic Drilling and Fastening
Bibliographic record
Abstract
Robotic applications in aerospace manufacturing and aircraft assembly today are limited. This is because most of the aircraft parts are relatively crowded and have complex shapes that make tasks like robotic drilling and fastening more challenging. These challenges include tool accessibility, path, and motion planning. In this paper, a process methodology was developed to overcome the tool accessibility challenges facing robotic drilling and riveting for aircraft parts that are located in crowded work environments. First, the tool accessibility was analyzed based on the global accessibility area (GAA) and the global accessibility volume (GAV) to determine the accessible boundaries for parts with zero, one, and two surfaces curvatures. Then, the path for the tool was generated while taking in consideration the approachability planning. This approach generates a number of intermediate points that enable the tool to maneuver around obstacles to reach the final target points if they are accessible. Last, a software application was developed to simulate the drilling and riveting tasks, and to validate the proposed process. The results of the simulation confirmed the proposed methodology and provided a numerical feedback describing the level of crowdedness of the work environment. The accessibility percentage can then be used by the design team to reduce the design complexity and increase the level of tool accessibility.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".