Module Selection Methodology for Designing Reconfigurable Machining Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a systematic, feature-based selection methodology to select the minimum yet sufficient set of modules (DP’s) to satisfy a given set of machinable features (FR’s) in order to construct a reconfigurable machining system capable of producing a part family. Two cases of selection are considered: selecting DP’s for single FR’s, and for multiple FR’s. The second case considers two selection scenarios: selecting separate DP’s to individually satisfy the FR’s, and selecting a single DP to simultaneously satisfy all the FR’s. These selection cases and scenarios are necessitated by our previous work done on developing a method for module identification. Each step of the methodology is presented in detail, and then subsequently illustrated by being applied to a case study. The case study deals with the design of the overall layout of a reconfigurable machining system required to machine a given family of die molds.
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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 it