Conceptual Design of an Attachment Based Reconfigurable Machine Tool Using Design Structure Matrix
Bibliographic record
Abstract
There has been very little research in the field of Reconfigurable machine Tools (RMTs). None of the past research developed a method to design a set of RMT configurations required to machine a part-family. This thesis presents a novel method to determine the functional specifications of the RMT configurations required to machine a part-family. The method is developed by firstly designing the RMT required to machine a single part. Thereafter, this method is extrapolated to suit the problem of a part-family. To design the RMTs for a single part, firstly, the part is decomposed into manufacturing features. Next, a novel method is developed to cluster the features. Each of these feature-clusters corresponds to a single RMT configuration. Based on the machining requirements of these RMT configurations, the modules are designed. These modules are assembled to form the final RMTs. The method is demonstrated by applying it to an example part.
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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) | 0.002 | 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 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".