Insights into microstructural evolution in functionally graded additive manufacturing of IN625–CuCrZr alloys: A CALPHAD-based thermodynamic analysis and experimental study on the role of metastable miscibility gap
Bibliographic record
Abstract
Functionally Graded Additive Manufacturing (FGAM) technique enables gradual and in-situ variation of composition within a component to achieve locally tailored microstructure and physical properties. This approach has recently been applied to fabricate multi-material components made of copper alloys for heat dissipation and Ni-based superalloys for superior mechanical properties, especially in aerospace applications. In this study, the successful FGAM of IN625 superalloy and CuCrZr alloy was demonstrated for the first time. Thin-wall samples without any major defects, such as cracks or lack of fusion, were fabricated by gradually varying the composition via a dual powder feeder laser directed energy deposition (L-DED) system. Further, to comprehend microstructural evolution during the process, CALPHAD simulations and characterization techniques (SEM, EDS, EBSD) were employed. A metastable miscibility gap was detected between the two alloys, which led to Cu-rich and Cu-lean regions in compositions with 50–75 wt.% CuCrZr. This resulted in mixed spheroidal and dendritic morphologies. X-CT analysis revealed Cu-lean spheroids clustering in specific regions, while EBSD showed coarse columnar grains in single-phase areas and fine equiaxed grains in compositions with a miscibility gap, where Cu-lean spheroids acted as nucleation sites. Scheil solidification simulations predicted M 2 C-type carbide formation within spheroids, confirmed by XRD, which contributed to a wide hardness variation of 180 to ∼800 HV across the component.
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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 | 0.001 | 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".