Dynamic deformation response of maraging steel 250 produced through directed energy deposition: Deformation behavior and constitutive model
Bibliographic record
Abstract
This study investigates the dynamic deformation response of maraging steel 250 (MS250) produced through directed energy deposition-arc (DED-Arc) across various strain rates and temperatures, aiming to develop constitutive models for reliable finite element simulations . Analytical transmission electron microscopy and scanning electron microscopy are subsequently performed to better understand the effects of microstructure features of DED-Arc built MS250 on their dynamic deformation behaviors . Experimental results reveal the variable thermal softening effects and combined impacts of strain rate and strain on the dynamic mechanical performance of as-deposited specimens. The heat-treated DED-Arc MS250 exhibits the synergistic influences of strain and strain rate, along with joint impacts of temperature and strain rate in its deformation characteristics in high-temperature regimes. Conventional Johnson-Cook models fail to capture these effects, causing discrepancies between predicted and experimental data for as-built and heat-treated DED-Arc MS250 alloys. In contrast, modified Johnson-Cook models tailored for each condition align closely with experimental results. Verification tests conducted under new impact conditions further validate the enhanced predictive capabilities of the modified models. Besides, as-deposited MS250 steel shows inferior flow stress and energy absorption, but post-fabrication heat treatment significantly improves its dynamic mechanical performance. The heat treatment also improves the resistance of heat-treated DED-Arc MS250 steel to forming adiabatic shear bands during room temperature impact tests, in comparison with the as-built condition. This improvement is associated with the formation of nano-sized, coherent, needle-shaped Ni 3 Mo precipitates, which balance strength and ductility.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".