Effect of Porosity on Tribological Properties of Medical-Grade 316L Stainless Steel Manufactured by Laser-Based Powder Bed Fusion
Bibliographic record
Abstract
The potential of laser-based powder bed fusion (L-PBF) technology for producing functional components relies on its capability of maintaining or even improving the mechanical properties of the processed material. This improvement is associated with the microstructure resulting from the high thermal gradient and fast cooling rate. However, this microstructural advantage may be counterbalanced by the lack of full density, which could be tolerated to a certain degree for applications such as biomedical implants and medical equipment. In this study, medical-grade 316L stainless steel specimens with porosities ranging from 1.7 to 9.1% were additively manufactured by L-PBF using different combinations of laser power and scanning speeds. Tribological properties were evaluated by pin-on-disc testing in dry conditions against a silicon nitride test body and analyzed in the context of microstructural characterization by optical and electron microscopy. The results reveal that higher porosity allows for a diminishing wear rate, which is explained by the capacity of the pores to retain wear debris related with the three-body abrasion. This research provides practical insights into the design of medical wear-resistant components, thereby enhancing our understanding of the potential of L-PBF in the fields of materials science and biomedical engineering.
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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".