Augmenting microstructure and tribological performance of wire arc additive manufactured PH13-8Mo stainless steel via TiC/TiB2 nano-particles incorporation
Bibliographic record
Abstract
This study aimed to investigate the effect of TiC and TiB 2 nano-inoculants addition on the microstructure and tribological behavior of PH13-8Mo stainless steel processed through wire arc additive manufacturing (WAAM). The incorporation of TiC/TiB 2 inoculants demonstrated notable efficiency in mitigating the anisotropic wear and scratch response and enhancing wear resistance observed in the as-printed state. This improvement was ascribed to the refinement of the grain structure, disruption of the columnar structure, and increased content of the retained austenite. Notably, TiB 2 inoculation exhibited superior grain refinement and achieved the highest hardness. However, the TiC-inoculated condition demonstrated the best wear resistance, attributed to its excellent combination of hardness and fracture resistance, and a higher contribution of strain-induced martensite transformation during wear testing. Additionally, the implementation of post-printing solutionizing and aging treatment was found to improve the scratch and wear resistance of the alloy, attributed to the formation of nano-sized β-NiAl precipitates. The main wear mechanism observed involved oxidation wear, adhesive wear, and three-body abrasive wear. The findings of this study highlight the significant potential of incorporating ceramic-based nano-particles for improving the wear resistance of WAAM PH13-8Mo components, particularly in demanding applications like injection molding dies where superior abrasion resistance is paramount.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".