Microstructure homogenization of laser powder bed fusion support-free low angle IN718 walls through heat treatment
Bibliographic record
Abstract
• Heat treatment homogenizes the microstructure of support-free IN718 walls. • Heat treatment yields similar microhardness of 482 HV (bulk) and 478 HV (downskin). • Precipitation hardening resolves the mismatch in hardness between bulk and downskin. This study investigates the effectiveness of heat treatment (HT) to homogenize the microstructural and mechanical asymmetry between the bulk and the downskin regions of support-free IN718 walls fabricated at angles of 30°, 20°, 15°, and 10° In the as-built condition, the microhardness ranged from 340 ± 5 HV to 351 ± 4 HV for the bulk and from 315 ± 4 HV to 323 ± 10 HV for the downskin region, resulting in a maximum difference range of 35 HV. The HT eliminated this difference where microhardness values of 482 ± 3 HV in the bulk and 478 ± 4 HV in the downskin were measured. The HT induced γ″ precipitation with volume fraction and mean precipitate size in the bulk of 16.6 % and 24.7 ± 7.3 nm. These values are statistically comparable to those in the downskin: 15.8 % and 26.5 ± 7.9 nm. The similarity in the γ″ characteristics explains the recovery of the mismatch in hardness as γ″ contributes approximately 85 % of the strengthening in the HT condition.
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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".