Selective compositional range exclusion via directed energy deposition to produce a defect-free Inconel 718/SS 316L functionally graded material
Bibliographic record
Abstract
In this research, additively manufacturing a functionally graded material (FGM) with a compositional range of Ni-based Inconel superalloy (Inconel 718) and Fe-based stainless steel (SS 316L) via directed energy deposition (DED) was examined. The microstructural transformation, defect behavior, and Vickers hardness of the material were each determined as a function of the discrete chemical composition of the FGM varied in steps of 10 wt% of the two materials across its length. In particular, for the specific compositions of 30 wt% Inconel 718/70 wt% SS 316L and 20 wt% Inconel 718/80 wt% SS 316L, critical pores and cracks (defects) initiated by ceramic oxides occurred due to the presence of intermetallic and carbide compounds. In addition, the results of electron backscatter diffraction (EBSD) and X-ray diffraction (XRD) analyses of the FGM demonstrate that the thermal and residual stresses due to constitutional supercooling and columnar-to-equiaxial transition (CET) became concentrated at the grain boundaries, thereby further contributing to the formation of the defects. The measured Vickers hardness was inevitably found to be minimal near the defective compositional range regardless of laser parameter optimization due to the reduced generation of segregants in the inter-dendritic regions and the increased formation of precipitates at the grain boundaries. The results of microstructural and mechanical analyses indicate that deliberate and strategic removal of the defective compositional range helped obtain a robust FGM composed of Inconel 718 and SS 316L without noticeable defects.
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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".