Optimization of the Post-Process Heat Treatment of Inconel 718 Superalloy Fabricated by Laser Powder Bed Fusion Process
Bibliographic record
Abstract
In the present study, multi-objective optimization is employed to develop the optimum heat treatments that can achieve both high-mechanical performance and non-distinctive crystallographic texture of 3D printed Inconel 718 (IN718) fabricated by laser powder bed fusion (LPBF). Heat treatments including homogenization at different soaking times (2, 2.5, 3, 3.5 and 4 h) at 1080 °C, followed by a 1 h solution treatment at 980 °C and the standard aging have been employed. 2.5 h is found to be the homogenization treatment threshold after which there is a depletion of hardening precipitate constituents (Nb and Ti) from the γ-matrix. However, a significant number of columnar grains with a high fraction (37.8%) of low-angle grain boundaries (LAGBs) have still been retained after the 2.5 h homogenization treatment. After a 4 h homogenization treatment, a fully recrystallized IN718 with a high fraction of annealing twins (87.1%) is obtained. 2.5 and 4 h homogenization treatments result in tensile properties exceeding those of the wrought IN718 at both RT and 650 °C. However, considering the texture requirements, it is found that the 4 h homogenization treatment offers the optimum treatment, which can be used to produce IN718 components offering a balanced combination of high mechanical properties and adequate microstructural isotropy.
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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".