Wear of 17-4 PH Stainless Steel Patterned Surfaces Fabricated Using Selective Laser Melting
Bibliographic record
Abstract
The recent developments in additive manufacturing (AM) are providing unprecedented opportunities in various fields, including the fabrication of advanced materials for tribological applications. The present work describes the results of an exploratory study focused on the analysis of 17-4 PH steel surfaces obtained using selective laser melting (SLM). In particular, the study includes the analysis of baseline (as-produced) and textured steel surfaces. Surface texturing comprises hexagonal prism structures (with or without dimples) arranged in a honeycomb pattern with 50 µm or 100 µm gap spacing. Starting from the minimum printing size enabled by the 3D printing platform, various textures are prepared by scaling up the characteristic dimensions of the prisms up to 500%. The obtained surface patterns are characterized (qualitatively and quantitatively) using a non-contact computerized numerical control (CNC) measuring system. The coefficient of friction (COF) was investigated using a Ball-on-Disk configuration using bearing steel balls as counterparts. For a fixed sliding speed, different contact loads and sliding radii were considered, while the tests were carried out in either dry or lubricant-impregnated conditions. The results of wear tests in both dry and lubricated conditions indicated that the baseline samples are provided with lower COF compared to the textured ones. For the latter, neither the gap spacing nor the presence of dimples led to significant variations in the COF. However, in lubricated conditions, the values of the COF for baseline and textured surfaces were closer and much smaller. In particular, the results provide clear indications regarding reducing the gap between prisms, which had a beneficial effect on the COF in lubricated conditions. Similarly, sensitivity to dimples was quite remarkable, with a reduction in the COF of about 30% when the larger gap spacing between the prisms was used.
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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".