On the microstructure and dynamic mechanical behavior of Cu–Cr–Zr alloy manufactured by high-power laser powder bed fusion
Bibliographic record
Abstract
• High-power laser powder bed fusion processing of Cu–Cr–Zr alloy. • Dynamic impact response of as-printed Cu–Cr–Zr alloy. • Achieving superior strength and toughness via dynamic impact loading. • Assessment of high strain rate effect on microstructure. This study explores high-power laser powder bed fusion (LPBF) processing of Cu–Cr–Zr alloy, focusing on its high strain rate dynamic mechanical response and microstructural evolution. The alloy undergoes significant strain hardening during dynamic impact loading, primarily attributed to intensified dislocation interactions and multiplication. This is accompanied by thermal softening induced by adiabatic heating, therefore improving strain accommodation. As the strain rate increases from 4400 s −1 to 11300 s −1 , the ultimate compressive strength (UCS) enhances from 173 ± 8 MPa to 489 ± 14 MPa, demonstrating a high strain rate sensitivity (SRS) of ∼ 1. Microstructural examinations reveal that higher strain rates intensify the occurrence of adiabatic shear bands (ASBs), leading to severe localized plastic deformation. These ASBs generate localized stress concentrations, which in turn accelerate crack initiation and propagation through pore formation and coalescence within the ASBs. Despite this severe plastic deformation, texture analysis indicates that the crystallographic texture remains largely stable which suggests that the deformation mechanism is primarily governed by dislocation motion and interaction, rather than by crystal structure reorientation. Overall, the alloy balances strain hardening and strain accommodation at high strain rates, making it well-suited for applications requiring strength and resilience under dynamic impacts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".