Laser Directed Energy Deposition-Based Additive Manufacturing of Fe20Cr5.5AlY from Single Tracks to Bulk Structures: Statistical Analysis, Process Optimization, and Characterization
Bibliographic record
Abstract
Laser directed energy deposition (LDED) can be deployed for depositing high-performance materials for various engineering applications. Alumina-forming steel is a high-performance material that possesses excellent corrosion and oxidation resistance, finding application in the power generation sector. In the present work, LDED using powder feeding (LDED-PF) was used to deposit Fe20Cr5.5AlY alloy using single-track, multi-track, and multi-layer deposition on SS 316L substrate. Response surface methodology (RSM)-based optimization was used to optimize the single-track deposition. The relationship between the track geometry parameters and the build rate with the LDED-PF processing parameters was studied. Further, the nonlinear relationship among the major process parameters was developed and an analysis of variance (ANOVA) was utilized to find significant parameters. The multi-track deposition yielded densely clad layers with a columnar grain structure. The presence of complex oxide slag of Y, Al, and Zr on the clad layer was detected. A micro-hardness of 240–285 HV was observed in the clad layer, with a hardness of 1088–1276 HV at the slag layer. The multi-layered structures showed a relative density of 99.7% with columnar growth and an average microhardness of 242 HV. The study paves the way for the deposition of dense alumina-forming steel structures for building components for power generation applications.
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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 | 0.000 |
| 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".