MATCHING MACHINE DESIGN TO THE PRODUCTION PROCESS: A CASE STUDY IN THE INTEGRATED DESIGN OF MOBILE EQUIPMENT AND MINING METHODS
Bibliographic record
Abstract
Underground mining imposes very rigid constraints on mobile equipment design. The choice of a particular mining “method” – i.e. the specific mix of techniques for excavation, ground support, and materials handling – is greatly influenced by the nature of the ore body being exploited. Mining methods tend to be fairly conservative, relying upon well established and proven equipment designs. In order to improve worker safety and productivity, South African platinum mines have increasingly turned to mechanization. An added benefit of these mechanization efforts is that the nature of the mining can be modified based on the feasible equipment designs. These efforts have resulted in changes to the mining methods employed in South Africa’s narrow-reef platinum group metal (PGM) ore bodies, as well as the development of a suite of mobile equipment which enables implementation of the new production processes. This paper focuses on the design and development of one of these machines - a narrow-reef bulldozer suited to selective mining. The resulting machine is a miniature unmanned bulldozer and multipurpose crawler platform designed for narrow-vein mining applications, with integrated mechatronics and remote control capabilities. This paper will discuss the development of the machine and the applications for which it was designed.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".