Effect of heat treatment on the dynamic impact response of Cu–Cr–Zr alloy manufactured by laser powder bed fusion
Bibliographic record
Abstract
This study investigates the effect of heat treatment on the dynamic impact behavior of a Cu–Cr–Zr alloy fabricated via high-power laser powder bed fusion (LPBF). Experiments utilized a split Hopkinson pressure bar (SHPB) setup with firing pressures of 100 kPa and 250 kPa, corresponding to maximum strain rates of 4400 s-1 and 11300 s-1 for as-built samples, and 1700 s-1 and 4700 s-1 for heat-treated samples. True stress-strain curves reveal a significant difference in strain accommodation mechanisms between as-built and heat-treated samples. Heat treatment markedly enhances the ultimate compressive strength (UCS) and work hardening rate under dynamic loading conditions, likely due to the Orowan strengthening mechanism by finely dispersed precipitates formed during heat treatment. The heat-treated samples exhibit continuous strength gains with increasing strain, reflecting pronounced strain hardening. In contrast, as-built samples show a plateau after reaching their UCS, where the activation of softening mechanisms, such as adiabatic shear band (ASB) formation, reduces the effectiveness of strain hardening. Despite the substantial changes in mechanical behavior, macro-texture analysis reveals minimal differences between as-built and heat-treated samples, suggesting that the performance disparities stem primarily from microstructural changes, such as precipitate formation and distribution in heat-treated samples, rather than shifts in crystallographic orientation.
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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 | 0.000 |
| 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 | 0.000 | 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".