High-pressure torsion effect on microstructural and hardness properties of Magnesium with Silicon Carbide nanoparticles
Bibliographic record
Abstract
Without a doubt, lightweight materials of high strength are in high demand in the automotive, aerospace, biomedical, and other industries that require such materials. Processing or manufacturing such materials has been a vital topic in contemporary research, as well as material development in the industry. A possible solution for the processing of lightweight materials of high strength is to target lightweight materials by nature such as magnesium and improve their mechanical properties such as stiffness, strength, and hardness. The aforementioned properties are sometimes achieved by processing soft and light materials through High-Pressure Torsion. In this work, Magnesium with Silicon Carbide nanoparticles (Mg-SiC) was strengthened and hardened through the High-Pressure Torsion (HPT) processing technique. The samples were compressed with a pressure of 6.0 GPa and twisted at the rotating speed of 1 rpm with varying numbers of turns N = 0, N = 1, N = 5 and N = 10 at a temperature of 23°C. The processed samples were prepared for the experimental investigation of microstructural characterization and hardness test examinations. Microstructural results showed that grain refinements of material can be achieved through HPT processing methods, which reduced the average grain sizes of unprocessed (N = 0) Mg alloy samples from 149.9 µm to 27.1 µm after processing ten turns. However, hardness test results do not indicate any significant improvement after one HPT processing turn although homogeneity is attained at five processing turns within the nanocomposites.
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 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 | 0.000 | 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 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".