EFFECTS OF CHEMICAL COMPOSITION ON SOLIDIFICATION, MICROSTRUCTURE AND HARDNESS OF Co-Cr-W-Ni and Co-Cr-Mo-Ni ALLOY SYSTEMS
Bibliographic record
Abstract
This article presents a study of solidification behavior and associate microstructure as well as hardness of Co-Cr-W- Ni and Co-Cr-Mo-Ni alloy systems. The differential scanning calorimetry (DSC) technique is employed to determine the transformation temperatures of these alloys. The focus is on investigating the effects of each constituent of the alloys on their solidification behavior and associate microstructures. The hardness values of these alloys are also determined using a Wilson Series 2000 Rockwell Hardness Tester. It is found that chemical composition influences the solidification behavior, associate microstructures and hardness of cobalt-based alloys significantly. Carbon content dominates the solidification behavior of these alloys when the contents of the solution-strengthening elements Mo and Ni are within their saturation in the solution matrix. However, as the contents of Mo and Ni reach a certain level, formation of intermetallic compounds changes the solidification behavior of these alloys remarkably. The presence of boron greatly decreases the solidification temperature. The volume fraction of carbides, Laves phase and other intermetallic compounds in the microstructure determines the hardness of the alloys.
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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".