Effect of solution and aging treatments on the mechanical properties, and fracture behavior of additively manufactured maraging steel
Bibliographic record
Abstract
Regarding the importance of annealing and solution treatments on the final microhardness of maraging steel, this study investigates the impact of different treatment times and temperatures on the microhardness of additively manufactured maraging steel. Annealing temperatures ranged from 815°C to 845°C, annealing times from 60 to 120 min, aging temperatures from 540°C to 580°C, and aging times from 90 to 180 min. The study examines the unique features of the fractured surface and the behavior of maraging steel after these treatments, as well as the critical limits of these treatments on the microstructure and fracture behavior. Results show that the oxygen diffusion rate in additively manufactured material is significantly higher than in bulk material, leading to considerable oxide formation, especially in porosities, after heat treatment. This phenomenon could contribute to porosity regrowth, resulting in abnormal dimple sizes in the fractography of additively manufactured maraging steel. An aging temperature of 580°C is critical, leading to severe necking under tensile load, resulting in low UTS, microhardness, and elongation, despite extreme softness and brittle fracture behavior. Aging temperature is identified as the most effective parameter on microhardness, with increasing temperatures reducing it. Annealing time and temperature have the opposite effect, with maximum microhardness at lower aging and higher annealing temperatures.
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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 | 0.001 |
| 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".