Mechanical and Wear Behavior of High Performance CuNiCoSi Alloy
Bibliographic record
Abstract
Cu-Ni-Si-X alloys, known as Corson alloys, are used for many applications such as connector components, high-power electronics, and welding electrodes and are very significant industrial alloys.These alloys have been the most studied copper alloy group for a long time.In recent years, studies on the CuNiCoSi alloy, which is included in the Corson alloy group, have attracted attention.This alloy has advantages such as high strength, superior load-carrying capacity, good conductivity, toughness, improved wear resistance, and thermal stability.In this study, it was aimed to determine the effects of cobalt (Co) addition to the Cu-Ni-Si alloy system on mechanical properties and wear behavior and to compare with CuNi2Si alloy.In this context, solution and aging heat treatments were applied to CuNi2Si and CuNiCoSi alloys under the optimum conditions determined in the previous study, and then they were subjected to hardness, conductivity, and wear tests.According to the test results, it was determined that the CuNiCoSi material has higher hardness and conductivity than the CuNi2Si material.In the wear test performed under 20N load, CuNiCoSi material showed higher wear resistance compared to CuNi2Si material.At the same time, in the wear test performed under 20N load, it was determined that the CuNiCoSi material had a lower coefficient of friction than the CuNi2Si material.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".