Process Parameter Optimization of 2507 Super Duplex Stainless Steel Additively Manufactured by the Laser Powder Bed Fusion Technique
Bibliographic record
Abstract
Laser powder bed fusion is an attractive technology for producing high-strength stainless steel alloys. Among the stainless steels, 2507 super duplex stainless steel (2507 SDSS) is known for its excellent combination of corrosion resistance and high strength. Although there are some studies that aimed at optimizing the laser powder bed fusion (LPBF) printing parameters to print highly dense 2507 SDSS parts; However, a full optimization study is not reported yet. This study aims at optimizing the printing parameters for 2507 SDSS, namely: laser power, scan speed, and hatch distance. The response surface methodology was used in generating a detailed design of experiment to investigate the different pore formation types over a wide energy density range (22.22–428.87 J/mm3), examine the effects of each process parameter and their interactions on the resulting porosity, and identify an optimized parameter set for producing highly dense parts. Different process parameters showed different pore formation mechanisms, with lack-of-fusion, metallurgical or gas, and keyhole regimes being the most prevalent pore types identified. The lack-of-fusion pores are observed to decrease significantly with increasing the energy density at low values. However, a gradual increase in the keyhole pores was observed at higher energy densities. An optimal energy density process window from 68.24 to 126.67 J/mm3 is identified for manufacturing highly dense (≥99.6%) 2507 SDSS parts. Furthermore, an optimized printing parameter set at a laser power of 217.4 W, a scan speed of 1735.7 mm/s, and a hatch distance of 51.3 µm was identified, which was able to produce samples with 99.961% relative density. Using the optimized parameter set, the as-built 2507 SDSS sample had a ferrite phase fraction of 89.3% with a yield and ultimate tensile strength of 1115.4 ± 120.7 MPa and 1256.7 ± 181.9 MPa, respectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".