Optimal Module Selection for Preliminary Design of Reconfigurable Machine Tools
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".