Response of split Hopkinson pressure bars to end-surface damage
Bibliographic record
Abstract
SHPB testing is one of the most widely used methods to characterize materials at high strain rates in order to provide data for the development of constitutive models. However, to produce reliable results, great care most be taken in data acquisition and processing. For example, during a routine test series, our surface bars were damaged. Control tests done on samples made of a well characterized Al 6061-T6 showed a significant alteration of the response of the system. Therefore, a study was initiated to understand the influence of surface damage on the response of the Hopkinson bar system. The results obtained from the damaged bar were compared with those from a test series using gaps that simulate potential damage defects. Results showed a similarity between data generated by gaps and damaged bars and suggested the importance of maintaining bars to a high quality surface finish. Comparisons of 3 lubricants were also done. Preliminary results showed a variation on the response ranging from negligible to significant. Finally, the influence of surface finish roughness ranging from RA4 to RA60 was investigated.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".