On the Processability and Microstructural Evolution of CuCrZr in Multilayer Laser-Directed Energy Deposition Additive Manufacturing via Statistical and Experimental Methods
Bibliographic record
Abstract
Laser-directed energy deposition (LDED) is a promising technology for coating, repairing, and building near-net-shape 3D structures. However, the processing of copper alloys, specifically, has presented a significant challenge due to their low laser absorptivity at the 1060 nm laser wavelength and high thermal conductivity. This study undertook a methodical examination by employing a 2 kW disk laser, operating at a wavelength of 1064 nm, and a coaxial nozzle head to comprehensively examine the processability of the highly conductive CuCrZr alloy for expanding the range of materials that can be successfully processed using LDED. The investigation focuses not only on optimizing the input process parameters that are the laser power, scanning speed, powder feed rate, and overlap ratio, but also on planning the toolpath trajectory, as these factors were found to exert a substantial influence on processability, geometrical accuracy, and the occurrence of defects such as lack of fusion. The optimal toolpath trajectory discovered involved implementing a zigzag strategy combined with a 90° rotation of the scanning direction. Additionally, a start point rotation was considered between each layer to even out the deposition of the layers. Moreover, a contour with a radial path at the corners was introduced to enhance the overall trajectory. Based on the hierarchal experimental study, the appropriate ranges for the key process parameters that leads to 99.99% relative density have been identified. They were found to be from 1100 up to 2000 W for the laser power (P), and from 0.003 up to 0.016 g/mm for the amount of powder that is fed to the melt pool distance (F/V). Regarding the influence of process parameters on the microstructure of the samples with equal deposition height, it was observed that varying combinations of process parameters within the optimal processing window resulted in variations in grain size ranging from 105 to 215 µm.
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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 | 0.001 | 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) | 0.000 | 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 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".