High-power laser powder bed fusion of Cu–Cr–Zr alloy: A comprehensive study on statistical process optimization, microstructure, and mechanical properties
Bibliographic record
Abstract
This study explores high-power laser powder bed fusion (LPBF) of Cu–Cr–Zr alloy, focusing on optimizing process parameters to achieve high relative density ( RD ) and low surface roughness ( S a ). A Plackett-Burman design (PBD) identifies layer thickness, laser power, and scanning speed as the most significant parameters. A response surface method (RSM) with central composite design (CCD) further refines the process, yielding an optimized parameter set with an RD of 99.96 % and S a of 13.1 μm. After establishing the optimum process window, the process efficiency is examined by increasing layer thickness, demonstrating higher build rates while preserving high RD and acceptable S a . The samples are evaluated for mechanical properties and microstructural evolution . Microhardness mapping reveals a uniform hardness distribution , with values ranging from 90 to 94 HV. Microstructural analysis shows the grain morphology varies with process parameters; thinner layers tend to produce bimodal distributions, whereas thicker layers promote more uniform grain structures . Crystallographic analysis indicates a strong <001> texture in samples processed at high volumetric energy density ( VED ), while subgrain analysis highlights significant fractions of low-angle grain boundaries, reflecting residual stresses and high dislocation density .
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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 | 0.000 | 0.001 |
| 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) | 0.000 | 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".