Dynamic Modeling of Hydraulic Shovel Excavators for Geomaterials
Bibliographic record
Abstract
The hydraulic shovel excavator has found significant applications in surface mining, construction, and geotechnical operations due to its flexibility and mobility. The key to high availability and utilization of this shovel is adequate understanding of machine dynamics and machine-formation interactions among other technical, operating, safety, and economic factors. These shovels are capital intensive, complex in design and operation within severely constrained environments. Detailed dynamic modeling and analysis are required to understand their effective utilization for achieving efficient operating performance and economic useful lives. Previous attempts at solving these problems are limited because they do not provide knowledge on the resistive forces and moments for efficient excavation. In this paper, the Newton-Euler techniques are used to develop hydraulic shovel dynamic models with numerical examples. Detailed analysis of the results shows that: (1) the kinematics of the stick-bucket joint (joint 3) is the most critical and effective control of this joint and is important input into efficient excavation design and execution; and (2) the highest resistive moments occur between the duration of 1.5 and 2.0s after the start of formation excavation and the highest magnitudes are 1,500Nm (for stick), 900Nm (for bucket), and 600Nm (for boom). Based on these results, the path trajectories, dynamic velocity and acceleration profiles, and dimensioned parameters for optimum feed force, torques, and momentum of shovel boom-bucket assembly can be modeled and used for efficient excavation. The optimum digging forces and resistances for the hydraulic shovel excavator can also be modeled and used to predict optimum excavation performance.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".