Influence of Direct Aging on the Mechanical Behavior of Laser Welded 3D-Printed SAE 630 (17-4PH) Stainless Steel Parts
Bibliographic record
Abstract
Abstract This study investigates the impact of direct aging on the mechanical properties of 3D-printed and laser welded tensile samples made from SAE 630 (17-4PH) martensitic precipitation-hardened stainless steel. ASTM E8-22 flat subsized tensile specimens were produced using a commercially available industrial additive manufacturing system, utilizing a plastic-matrix-bound commercially available 17-4PH metal powder filament and 100% infill using OEM supplied printing parameters. Four conditions were evaluated, with two samples per condition to ensure data reproducibility. The sample conditions were as follows: (1) as-printed & sintered, (2) welded using an IPG LightWeld 2000 XR handheld laser welding machine (using AWS/SFA A5.9 ER630 filler wire), (3) direct aged per AMS 2759/3J H1150 after printing, and (4) laser-welded followed by direct aging. Tensile testing revealed that direct aging enhances ductility by 118% while reducing the tensile strength by only 21% and still meeting the strength requirements outlined in ASTM A564-13. Direct aging also shifted the failure location from the weld zone to the base material. Additionally, optical scanning was employed to assess dimensional stability due to residual stress evolution across the as-printed, welded, and heat-treated conditions. These findings demonstrate the effectiveness of direct aging in improving the performance of welded 3D-printed 17-4PH stainless steel components.
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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".