Effects of Available Energy and Impact Rate on Charpy Absorbed Energy in the Upper Shelf
Bibliographic record
Abstract
Abstract Advances in steelmaking technology have led to the introduction of structural steels with increased strength and toughness, to the extent that the energy absorbed by Charpy samples tested in the upper shelf region commonly approaches or exceeds the maximum available energy in conventional pendulum impact machines. In these circumstances, it becomes relevant to re-examine the validity of absorbed energy values measured in cases where the absorbed energy is a large fraction of the available energy. According to ASTM E 23 clause 10.1, “Absorbed energy values above 80 % of the scale range are inaccurate and shall be reported as approximate.” This is because “the velocity of the pendulum decreases…during fracture to the point that accurate impact energies are no longer obtained.” Although the 80 % limit has been accepted by consensus for decades, it has been challenged recently with the contention that excess capacities substantially larger than 20% are required to provide machine-independent values of impact energy. The present work was performed to test whether the amount of excess energy (i.e., the difference between available energy and absorbed energy) affects the Charpy absorbed energy in the upper shelf. Two structural steels typical of current linepipe grades have been used in the project, an experimental steel and a commercial steel, both with bainite/ferrite microstructures but with different toughnesses. Charpy samples have been tested at impact rate in pendulum and drop-weight machines. The results confirm the acceptability of absorbed energy values up to 80 % of the machine capacity, in support of the requirement stated in ASTM E 23.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".