Dislocations mobility in superalloy-steel hybrid components produced using wire arc additive manufacturing
Bibliographic record
Abstract
A hybrid component consisting of Inconel 718 superalloy (IN718) and mild/low structural steel (grade S275) was fabricated using the wire + arc additive manufacturing (WAAM) technology to evaluate the feasibility, texture, and the corresponding characteristics. S275 is considered as a mild/low alloy steel that is compatible with IN718 and can serve as a mechanically under-matched substrate for WAAM deposition with potential use in the petrochemical industry. Characterization of the interfacial hybrid part was conducted through Scanning Electron Microscopy (SEM), Energy Dispersive Spectroscopy (EDS), and Electron Backscatter Diffraction (EBSD) in three different states of as-built (AB), solution-treated (ST), and aged (STA) conditions. EDS elemental mapping confirmed the presence of Laves closer to the interface even after 1-hour solutionizing at 1080 °C. Solution-treatment resulted in eliminating microsegregation (mainly Nb) and considerable Laves dissolution, along with a significant decrease of the hardness in both WAAM-deposited IN718 and the substrate. The bulk texture of WAAM-deposited IN718 was measured by neutron diffraction in all three states of AB, ST, and STA, showing a strong 〈0 0 2〉 texture parallel to the building direction (BD). Elastic-field mathematical models were used to interpret the role of heat treatment in perfect-, and partial dislocations’ mobility and Peierls-Nabarro stress by considering the neutron diffraction and nanohardness data collected across the interface. Limiting aspects associated with dissimilar joining of IN718 and S275 alongside post-processing heat-treatments were pointed. Recommendations were made to facilitate possible additive repair of IN718 hybrid parts for various industrial applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".