Effects of Silicon Content on the Microstructure and Mechanical Properties of Cobalt-Based Tribaloy Alloys
Bibliographic record
Abstract
Cobalt-based Tribaloy alloys are strengthened mainly by a hard, intermetallic Laves phase consisting of Co3Mo2Si or/and CoMoSi; therefore, silicon content plays a large role in the microstructure and performance of these materials. In this research, the microstructures of two cobalt-based Tribaloy alloys that are largely different in Si content are studied using scanning electron microscopy (SEM) with an EDAX energy dispersive X-ray (EDX) spectroscopy, and X-ray diffraction (XRD), fatigue strength under rotating-bending test, mechanical behavior under nanoindentation, and hardness at room and elevated temperatures using a microindentation tester. It is revealed that with higher silicon content (2.6 wt. %), T-400 has a hypereutectic microstructure with Laves phase as primary phase, whereas with lower silicon content (1.2 wt. %), T-401 has a hypoeutectic microstructure with solid solution as primary phase. T-400, containing lager volume fraction of Laves phase, exhibits better fatigue strength, in particular, at high stresses, while T-401, with less volume fraction of Laves phase, has improved ductility, exhibiting better resistance to fatigue at low stresses. The hardness of both alloys decreases with temperature, and T-401 shows higher reduction rate. T-400 is harder than T-401.
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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) | 0.000 | 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 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".