Digging force and power consumption during robotic excavation of cable shovel: experimental study and DEM simulation
Bibliographic record
Abstract
Cable shovels are on the top priority of the most widely used machinery in open-pit mining industry, the automation of which offers great potential to improve both production efficiency and equipment reliability. Rational evaluations of digging force and power consumption serve as one of the fundamental techniques of realising autonomous operation of cable shovels. In this study, because of the wide range of digging parameters in theoretical calculation, the method of simulation is used to narrow the range of digging parameters in theoretical calculation, so that the digging force can be accurately and efficiently predicted by the method of theoretical calculation. Furthermore, scale-model-based experiments were taken in order to validate the effectiveness of the simulation results. Conclusively, although the theoretical calculation can numerically predict the power consumption in an acceptable extent (R2>0.85), the fitted value of unit resistance to excavation for the theoretical calculation was out of its empirical value range according to the classical theory applied to the prediction of digging resistance in the design of cable shovel. On the other hand, the simulation results were shown to be highly consistent with the experimental results (R2>0.9), which demonstrate the efficiency of the simulation method in evaluating dynamic working performance of cable shovels.
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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 | 0.001 | 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".