Development of a thermal excitation source used in an active thermographic UAV platform
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This work aims to address the effectiveness and challenges of using active infrared thermography (IRT) onboard an unmanned aerial vehicle (UAV) platform. The work seeks to assess the performance of small low-powered forms of excitation which are suitable for active thermography and the ability to locate subsurface defects on composites. An excitation source in multiple 250 W lamps is mounted onto a UAV and is solely battery powered with a remote trigger to power cycle them. Multiple experiments address the interference from the UAV whilst performing an active IRT inspection. The optimal distances and time required for a UAV inspection using IRT are calculated. Multiple signal processing techniques are used to analyse the composites which help locate the sub-surface defects. It was observed that a UAV can successfully carry the required sensors and equipment for an Active thermographic NDT inspection which can provide access to difficult areas. Most active thermographic inspection equipment is large, heavy, and expensive. Furthermore, using such equipment for the inspection of complex structures is time-consuming. For example, a cherry picker would be required to inspect the tail of an aircraft. This solution looks to assist engineers in inspecting complex composite structures and could potentially significantly reduce the time and cost of a routine inspection.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it