Structure design and service performance of bionic conical pick
Bibliographic record
Abstract
Heavy equipment such as continuous mining machines and shearers are widely used in coal mining projects, and conical picks are essential cutting tools. During the cutting process, different conical pick structures can have significant impacts on the cutting performance of the equipment, and the wear resistance of the conical picks will directly affect the efficiency and reliability of the equipment. In this paper, a bionic dimple-structured conical pick is designed, and a mathematical model of the dimple structure is established. The cutting performance, self-rotation performance, and wear resistance of dimple-type picks (DTPs) and ordinary-type picks (OTPs) are studied using the discrete element software EDEM and the dynamic analysis software ADAMS. The results show that during the cutting process, the trend of rotational torque over time for both types of conical picks first increases and then decreases, with the maximum rotational torque of DTP being greater than that of OTP, indicating that the self-rotation performance of DTP is superior to OTP. Compared with OTP, DTP exhibits a smaller total wear amount and better wear resistance performance. When comparing end plates equipped with DTP to those with OTP, the end plates with DTP have a smaller load fluctuation coefficient, lower cutting resistance, lower specific energy consumption for cutting, and greater total coal mass.
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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".