Mechanical and Material Characterization of Mining Wheels for Enhanced Safety
Bibliographic record
Abstract
A study was undertaken to evaluate the mechanical and material behaviour of the Q345 alloy, used in fabrication of five-piece mining wheel assemblies. Material samples were extracted from all components of a five-piece wheel. Material testing included compositional analyses, fully submerged corrosion testing, and microstructural analyses. Mechanical testing included hardness testing, tensile testing and stress-based high-cycle fatigue testing of specimens with polished and pre-corroded surface conditions. Special emphasis was placed on obtaining the fatigue behaviour of the alloy in the pre-corroded condition. Component microstructures were all found to consist of ferrite and colony pearlite. Ultimate tensile strengths of most component samples ranged from 471 MPa to 544 MPa, which was within minimum alloy specifications. Fatigue results found polished specimens and pre-corroded specimens to have endurance strengths of approximately 295 MPa and 222 MPa, respectively, at 5,000,000 cycles. The pre-corroded condition resulted in a decrease in fatigue strength of 25.6%.
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 | 0.001 |
| 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) | 0.002 | 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".