HdH Composite Assembly & Mobile Automation
Bibliographic record
Abstract
Hawker de Havilland has undertaken research & development initiatives to utilise off-the-shelf automation technology to improve the functionality and efficiency of aerospace assembly processes. Several applications have been demonstrated, including the assembly of composite ribs and skins prior to consolidation, the drilling and trimming of composite components in preparation for assembly operations and the drilling of assemblies. Through all of these demonstrations, the same technology base has been applied – that of industrial robotics. This paper will present the technical aspects of two, individual problems and the solutions implemented. The problem and solution for the application of the assembly of dry, reinforced composite ribs to skins in a unitised box prior to consolidation of the composite assembly will be presented. The study will include detailed requirements and the control solution via force-torque sensor integration and manipulation. This paper will also present a means in which to improve the utilisation of the industrial robotic solutions discussed via a flexible mobile platform. Detail will be included on the factory integration, method of operations and the safety aspects that have been addressed. Also included will be the methods of indexing the mobile automation to the work piece.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.427 | 0.193 |
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".