Assessment of Microstructural and Mechanical Properties of 420 Stainless Steel Fabricated by Laser Powder Bed Fusion
Bibliographic record
Abstract
In this work, fabricated samples of additively manufactured 420 stainless steel (420SS) via laser powder bed fusion (LPBF) process were analyzed. The microstructural features, grain morphology, crystallographic texture, phase composition, and tensile properties were evaluated for both as-printed and heat-treated conditions. The as-printed condition exhibits a yield strength of 1083 MPa and a tensile elongation of 21.2%. Microstructure analysis revealed that its comparable ductility is due to the presence of 15.2 % of retained austenite. After the successive heat treatment procedure, the yield strength improved significantly to 1388 MPa while decreasing the tensile elongation to 12.4 %. The yield strength obtained in the heat-treated condition was superior to previously reported literature values of precipitation hardening stainless steels fabricated by LPBF, and 420SS fabricated using different additive manufacturing processes. This improvement in yield strength is attributed to the coarsening of martensite laths and needles, elimination of retained austenite phase, and the carbide precipitation of 1.9 vol. % in the microstructure. The results in this work proved that the tensile properties and microstructure were greatly influenced by laser parameters and can be tailored accordingly using different heat treatment techniques.
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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".