Controlled single and repeated impact testing for material plastic behaviour characterisation under high strain rates
Bibliographic record
Abstract
Abstract Instrumented single‐shot experiments provide crucial information of a material's response to impact events that can be used in shot‐peening modelling. However, no authors successfully used such test for constitutive model identification and validation as existing test rig generally cannot provide an accurate determination of the shot trajectory in three dimensions over a wide velocity range. In this work, a shot‐peening test rig that can propel single shot under the process conditions with a high aiming accuracy is presented. The test rig propels industrial shot by sudden pressurised gas release. A methodology to recover the propelled shot three‐dimensional trajectory within a 200‐μm accuracy using two high‐frequency cameras is developed in an open‐source in‐house code. The test rig can propel 0.5‐, 1.19‐ and 2.5‐mm‐diameter shot at velocity ranging from 0.8 to 143 m s−1 and can send several shots at the same position when using the largest shot diameter. Two potential applications of the set‐up are presented for (i) coefficient of restitution measurement with different shooting angles and velocities and (ii) crystal plasticity finite element model validation using the impact dent topology, the shot displacement curve and the crystal misorientation field under the dent.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".