Structure analysis of a dragline tooth and its wear prediction
Bibliographic record
Abstract
Dragline teeth are used to dig overburden rocks and fill the bucket by its action in surface mining. Thus, these teeth are very important for a better performance of the dragline. In the present investigation, efforts were made to determine the failure zone within the teeth through modeling, using the finite element software ANSYS workbench. The maximum deformation and maximum von-Mises stress were 0.286 mm and 801.38 MPa, respectively. From fatigue analysis, the minimum tool life was 24 540 cycle and the minimum factor of safety was 0.1 at the tip of the tool. Beyond the cutting edge (tip), the factor of safety was greater than 1.13. Finite element analysis was extended by varying the working load on the edge of the tool (68–86 tonnes) as well as the cutting angle (30°–36°). It was found that if the working load was increased, both corresponding maximum deformation and maximum von-Mises stress were increased, while the factor of safety was decreased. In the scanning electron microscopy analysis, wear phenomena such as rock intermixed, fracture of WC-grain, oxidized WC-grain, plastic deformation, cavity formation, cracking, and crushing were visible at magnification of 1000×.
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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 | 0.000 |
| 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 | 0.001 | 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".