Optimal Module Selection for Preliminary Design of Reconfigurable Machine Tools
Why this work is in the frame
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Bibliographic record
Abstract
Presented in this paper is a feature-based method for selecting an optimal (minimum yet sufficient) set of modules necessary to form a reconfigurable machine tool for producing a part family. This method consists of two parts. In the first part, a feature-module database is created to form a selection space, where the machinable geometric features identified in STEP are defined as functional requirements (FR’s) and the structural component modules derived from the conventional machine tools as design parameters (DP’s). An inner FR-to-DP mapping mechanism within the database is based on the “Membership Grade Matrix,” which defines metrics to quantify the degree of association between a FR and a DP. Within the confines of the selection space built upon this FR-DP database, the second part of the method involves a two-step procedure for module selection. The first step is to select the modules from this space to construct all the required individual configurations of the reconfigurable machine tool. The second step is to maximize the number of common modules among the originally selected modules through re-selection. A case study on designing a reconfigurable machine tool dedicated to a given family of die molds is conducted and discussed.
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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 | 0.001 | 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 | 0.001 |
| 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