Feature-based machining precedence reasoning for prismatic parts in CNC process planning
Bibliographic record
Abstract
Precedence relations are crucial to operation sequence planning, and must be satisfied in determining an operation sequence. Most of the previous research work has focused on how to optimize operation sequence and assume that the precedence relations are given as input, or specified interactively by the users. However, to obtain precedence relations, especially to automatically generate precedence relations using knowledge of feature interactions imposes an interesting challenge for Computer Aided Process Planning (CAPP). This paper presents a definition for feature accessibility and a method to obtain its geometric precedence relations using the updated feature iMaginary face-Real face (M-R face) dependency and the accessibility of the cutting tool to the feature. This method allows the dynamic generation of geometric precedence relations based on the setup direction, updated topologies and interactions between the features. The geometric precedence relations could be influenced by the reference precedence form tolerance specifications and the machining expertise precedence relation input from matching experts. The precedence-reasoning module is currently being implemented within a CNC computer-aided process planning system for prismatic parts.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".