A study on the microstructural characteristics of 3D printed 316l stainless steel by selective laser melting
Bibliographic record
Abstract
The metal AM market is tipped to undergo relentless growth in the coming years, with an expected market increase from $2.9 billion USD in 2022 to $15.0 billion USD in 2032. The ability to direct-manufacture complex porous structures is a powerful tool. Paired with the ability to produce patient-specific metallic implants without the need for specialised tooling, it is obvious that AM can positively transform the industry. Selective Laser Melting (SLM) is one method currently used to produce metallic AM components and will be the sole manufacturing method explored in this work. SLM is a complex manufacturing process, with over 100 parameters affecting component quality. This work focuses on investigating the microstructure and defects present within samples of 316L Stainless Steel manufactured by SLM. The findings will be used to explain the difference in fatigue performance between SLM and wrought 316L, which is currently work in progress.
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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) | 0.002 | 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".