On The Development Of A Strength Prediction Methodology For Fibre Metal Laminates In Pin Bearing
Bibliographic record
Abstract
The development of Fibre Metal Laminates (FMLs) for application into aerospace structures represents a paradigm shift in airframe and material technology. By consolidating both monlithic metallic alloys and fibre reinforced composite layers, a new material structure is born exhibiting desired qualities emerging from its heterogeneous consituency. When mechanically fastened via pins, bolts and rivets, these laminated materials develop damage and ultimately fail via mechanisms that were not entirely understood and different than either their metallic or composite constituents. The development of a predictive methodology capable of characterizing how FMLs fastened with pins behave and fail would drastically reduce the amount of experimentation necessary for material qualification and be an invaluable design tool. The body of this thesis discusses the extension of the characteristic dimension approach to FMLS and the subsequent development of a new failure mechanism as part of a progressive damage infinte element (FE) modeling methodology with yielding, delamination and buckling representing the central tenets of the new mechanism. This yielding through delamination buckling (YDB) mechanism and progressive FE model were investigated through multiple experimental studies. The experimental investigations required the development of a protocol with emphasis on measuring deformation on a local scheme in addition to a global one. With the extended protocol employed, complete characterization of the material response was possbile and a new definition for yield in a pin bearing configuration was developed and subsequently extended to a tensile testing configuraiton. The performance of this yield definition was compared directly to existing definitions and was shown to be effective in both quasi-isotropic and orthotropic materials. The results of the experiments and FE simulations demonstrated that yielding (according to the new definition), buckling and delamination resulting in joint collapse and failure have all occurred within the stipulated predictions of the YDB mechanism.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".