Characterisation of Aluminium Matrix Syntactic Foams under Static and Dynamic Loading
Bibliographic record
Abstract
Development of a lightweight, strong and energy-absorbing material that has potential application for the protection of vehicles and occupants against impact and blast, is a difficult challenge facing the materials community. Aluminium matrix syntactic foams will be investigated as a possible core material as part of a multi-layered protection system for military vehicles. Aluminium matrix syntactic foams are composite materials consisting of an aluminium matrix implanted with hollow or porous ceramic particles. This paper investigates the mechanical properties of aluminium matrix syntactic foam with different sizes of ceramic micro-spheres and different grades of aluminium, fabricated by the pressure infiltration method. The static crushing behaviour of the foam was investigated under two test conditions using an Instron 4505 machine. Results are compared and discussed. The dynamic compressive response was investigated using a drop-weight impact test machine. It was found that the particle size of the ceramic micro-spheres and the grade of the aluminium metal have a significant effect on the energy absorption capacity of the material. The compressive strength of the syntactic foam was found to increase with increasing compressive strength of the metal matrix.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".