Bibliographic record
Abstract
Abstract As advanced composite structures become larger and more complex, there is a corresponding need to improve methods of joining and assembly. Current methods of joining composite materials for aerospace applications, that is, adhesive bonding and mechanical fastening, present some design and manufacturing limitations. The mechanical fastening method cannot be effectively applied to composite structures because of stress concentration, the effects of drilling on the structural integrity, and localized delamination. Poor bonding properties between adhesives and polymers make the adhesive bonding methods less desirable for most structural applications. The fact that thermoplastic materials can be remelted provides the opportunity of welding (or fusion bonding) of thermoplastic composite parts as an alternative to joining and assembly. Fusion bonding, in principle, consists of surface preparation, heating the polymer at the weld interface to a viscous state, physically causing polymer chains to interdiffuse across the interface, and cooling the polymer for consolidation. The polymer chains are intertwined across the interface during the welding process, resulting in disappearance of the bonded surface and improving the ability of transferring loads through the welded area. The quality of the welded parts is usually compared to that of autoclave consolidated or compression molded parts.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".