Simplified model for the design of composite sandwich construction
Bibliographic record
Abstract
Over the past few decades composite materials consistently offered elegant solutions alleviating existing challenges in various industries. For example, major aircraft manufacturers such as Boeing, Bombardier and Airbus increased the share of composite materials utilization into their designs. Continuous progress in composites research and manufacturing technology resulted in making them more appealing solution to design engineers in virtually all industries. As early as the 1960s, composites were used to build parts of the ship superstructure. Since then the percentage of composite structures in ship superstructure is continuously increasing due to their many advantages [1]. Meanwhile the relatively high cost of composites manufacturing and assembly has limited them to military applications [1]. High specific strength requirements implied advanced composites technologies, e.g. autoclaving, since acquiring “one” commercial size autoclave is a substantial investment in the order of millions of dollars. Consequently commercial maritime applications from fishing vessels to bulk carriers are still dominantly utilizing steel and aluminum superstructure. On the other hand current advances in composites manufacturing including the introduction of new resin technologies can provide efficient solutions that overcome the high cost of manufacturing while maintaining their superior strength characteristics. Recent advances in composites manufacturing methods provide new Out-of-Autoclave (OoA) composites at a lower manufacture cost without sacrificing the strength advantages of advanced composites [2]. This is due to the fact that OoA composites do not require the use of autoclave by utilizing enhanced resin technologies. In other words using OoA composites eliminates the high cost associated to acquiring an autoclave as well as its overhead cost.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.024 | 0.004 |
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".