Investigation of the gouging abrasion resistance of materials in the mining industry
Bibliographic record
Abstract
With increased budget constraints, innovative cost reduction methods are required to increase the profitability of today's mines. Abrasive wear reduction is a novel way to reduce costs and increase productivity. Specifically, gouging abrasion is making an increased contribution to abrasive wear losses in the oil sands industry. To assess material property requirements for mitigating this wear mechanism, jaw crusher gouging abrasion tests using a modified ASTM G81 procedure, have been carried out on a range of wear materials of interest for oil sands mining service. The method involves a comparison of the wear losses that occur for reference and selected test plates when a controlled amount of standard feed rock is comminuted in a laboratory jaw crusher. Among the classes of material evaluated have been Q&T plate steels, austenitic manganese steel, chromium and chromium molybdenum white irons as plain castings and in laminated forms and also chromium carbide and tungsten carbide overlaid wear plates. In addition, the initial stages of relationships are presented relating wear rates/factor, determined from the laboratory gouging abrasion test, to the quartz content of the abrasive material. Of all the materials tested, the laminated CrMo white consistently had the lowest wear factor (best gouging abrasion resistance). From the data produced by this work, the wear factor has a linear relationship with quartz, while the wear rate has a non-linear relationship.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".