Tensile/Compressive Response of 316L Stainless Steel Fabricated by Additive Manufacturing
Bibliographic record
Abstract
Additive manufacturing has evolved from a rapid prototyping technology to a technology with the ability to produce highly complex parts with superior mechanical properties than those obtained conventionally. The processing of metallic powders by means of a laser makes it possible to process any type of alloy and even metal matrix composites. The present work analyzes the tensile and compressive response of 316L stainless steel processed by laser-based powder bed fusion. The resulting microstructure was evaluated by optical microscopy. Regarding the mechanical properties, the yield strength, ultimate tensile strength, percentage of elongation before breakage, compressive strength and microhardness were determined. The results show that the microstructure is constituted by stacked micro molten pools, within which cellular sub-grains are formed due to the high thermal gradient and solidification rate. The compressive strength (1511.88 ± 9.22 MPa) is higher than the tensile strength (634.80 ± 11.62 MPa). This difference is mainly associated with strain hardening and the presence of residual stresses. The initial microhardness was 206.24 ± 11.96 HV; after the compression test, the hardness increased by 23%.
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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".