Assessing Composite and Fibre Metal Laminate Materials for Automotive Applications Through Impact and Quasi-Static Indentation Testing
Bibliographic record
Abstract
Automakers can conform to pressures of increasing gas mileage and reducing emissions through weight reduction of vehicles with the use of composite and fibre metal laminate (FML) materials.An important safety equivalency characteristic of these lightweight materials that must be studied is their impact resistance compared to that of traditionally used automotive materials; steel and aluminum.Low-velocity impact (LVI) and quasi-static indentation (QSI) testing was conducted on thin composite and FML panels to assess their applicability in impact prone automotive components.To aide in the impact assessment of the lightweight materials a novel approach was developed to determine the strain and visible damage evolution within specimens through the use of digital imaging correlation (DIC) technology in quasi-static indentation tests.Simulating dynamic impact events with quasi-static loads was also evaluated and its limitations were discussed.The impact characteristics of monolithic aluminum 2024-T3 sheet outperformed carbon/epoxy, carbon/nylon, CARAL 5 2/1-0.3,and GLARE 5 2/1-0.3lightweight panels.Due to the strain rate strengthening effects of glass fibres, GLARE was proven to be the best alternative to aluminum for automotive applications.The use of quasi-static loading to replicate dynamic impacts was validated for CARAL, aluminum 2024-T3, and GLARE.DIC technology was successfully implemented in QSI tests where full field deformation data provided unmatched detail of visible damage and crack initiation progression.
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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 | 0.000 |
| 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) | 0.002 | 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".