A MULTI-LAYER NEURO-FUZZY NETWORK FOR TOOL-PIECE COLLISIONS DETECTION IN CAD/CAM
Bibliographic record
Abstract
In Computer Aided Design and Manufacturing (CAD/CAM), the technicians have often been confronted with situations of collisions on a manufacturing site, between mobile elements and static elements of the machine. The objective of this study is to develop a neuro-fuzzy technology for the recognition of machining states in CAD/CAM, in particular, the collision states between different profiles, and the treatment after a collision detection. This work is divided in two parts; the first is to design a multiple layers neural-network, which after training evaluates the quality of a machining state at a point of the profile, and therefore the probability of existence of a collision between two profiles. The second part is a design of a fuzzy system that intervenes after the passage of the first tool. If there was a bad quality of machining, the system decides to pass one or several other tools, in order to find the necessary tools for this phase and the corresponding zones, while preventing collisions.
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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".