Wire and arc additive manufacturing of 316L stainless steel/Inconel 625 functionally graded material: development and characterization
Bibliographic record
Abstract
In this work, a 316L stainless steel to Inconel 625 functionally graded material (FGM) was built using different deposition strategies (named as direct and smooth-type interfaces) by Twin-Wire and Arc Additive Manufacturing (T-WAAM). This combination of materials is of interest in chemical plants, oil & gas, and nuclear applications, where high corrosion and wear resistance are essential requirements. Although these properties are superior in Inconel 625, replacing Inconel with stainless steel in strategic regions of structural components can reduce the overall costs and parts’ weight. Both direct and smooth transition interfaces were tested and characterized. Microscopic analysis revealed that each interface and the as-built samples had an austenitic matrix, and every sample was well bonded and free of defects. Different types of microstructures evolved at the interfaces due to distinct gradients in composition. Synchrotron X-ray diffraction measurements showed that the smooth-gradient produced secondary phases, such as δ-phase (Ni3Nb) and carbides, that were not present with the direct interface strategy. Overall, the properties were superior in the FGM with a direct interface, which experienced higher strengths and elongations upon failure. Moreover, neutron diffraction measurements revealed that lower residual stresses developed in the direct interface FGM than in the smooth gradient FGM.
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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 | 0.001 | 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) | 0.001 | 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".