Microstructural evolution and high-temperature deformation behavior of wire arc additively manufactured Inconel 718 forging Preforms: Toward a hybrid Additive–Forging Process
Bibliographic record
Abstract
Hybrid manufacturing routes combine additive manufacturing (AM) with conventional methods. They offer a potentially faster, more economical pathway to produce engineered components with performance that equals or exceeds that of wrought or cast counterparts. In these strategies, AM allows fabrication of preform geometries without the need for custom tooling or feedstock. Conventional post-processing mitigates AM-specific issues such as anisotropic mechanical properties, residual stresses, porosity, and the presence of large columnar grains with pronounced texture. This study focuses on a hybrid AM-forging approach, in which the hot deformation behaviour of wire arc additive manufacturing (WAAM) processed Inconel 718 preforms was evaluated using hot compression tests (HCT). Cylindrical samples from WAAM deposited walls were hot compressed in a Gleeble® 3800 physical simulator at 927-1100 °C and strain rates of 0.01-5 s -1 . The evolution of microstructural anisotropy and flow behavior under these conditions was examined using optical microscopy (OM), field emission scanning electron microscopy (FE-SEM), energy dispersive spectroscopy (EDS), and electron backscatter diffraction (EBSD). Dynamic recrystallization (DRX) was dominant in specimens deformed at 5 s -1 , while dynamic recovery (DRV) prevailed at 0.01s -1 . The size of recrystallized grains during hot deformation was predicted using a phenomenological model based on the Zener-Hollomon parameter. The results revealed that grain size varies as a function of strain, enabling the tailoring of the grain structure of components forged from AM preforms. Processing maps indicated a power dissipation efficiency (η) of ∼0.33 in a stable hot-working regime, consistent with DRX-dominated microstructural refinement.
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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) | 0.000 | 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 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".