Computational Simulation and Experimental Analysis on Wearing Mechanisms of Gypsum and Concrete Samples in Pin‐on‐Disk ASTM Abrasion Testing
Bibliographic record
Abstract
ABSTRACT Mechanical excavation machines, like continuous miners and road headers, have been broadly used in tunneling and underground and surface mines. The disc cutters are seated on the different cutter heads’ to cut different parts of the tunnel face. With the increase in the cutters’ size and power, the cutting disc cutters’ capacity has been extended to cut moderate and tough rock types. This experimental and numerical research includes the application of, “Pin‐on‐Disk” ASTM abrasion testing, in which the failure mechanism of an interface between both the rock‐like samples and (WC–Co) tungsten carbide has been investigated under different confining pressures. The research aims to investigate the wear mechanism of gypsum and concrete samples. The Particle Flow Code in three dimensions (PFC3D) was used for test simulations concurrently with the experimental setup. A drilling pin with a diameter of 0.4 m was positioned above the model. The pin was inserted into the model at speeds of 0.01 mm/s at depths of 1, 3, and 5 m. A total of nine lab tests were conducted. The tensile strength of the material was 2.5 MPa. The results show that the values of volume lost for the gypsum and concrete discs were detected as a function of sliding length, fitting to non‐linear behavior. The wearing depth increased by increasing the loading force. Under constant loading force, the gypsum sample wears more than the concrete sample because gypsum is less strong than concrete. The PFC generates useful findings that experimental tests cannot provide.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".