Modular architecture design of reconfigurable machine tools for agile manufacturing
Bibliographic record
Abstract
In industry, agile manufacturing is gaining more and more attention. To support agile manufacturing, reconfigurable machine tool has been introduced, which can be reconfigured to satisfy different machining requirements. In the early stage of reconfigurable machine tool design, machine tool architecture design is an important issue. Due to the lack of information in the early design stage, it is difficult to generate and select feasible machine tool architecture solutions. In light of this challenge, the purpose of this thesis is to develop a systematic modular architecture design methodology for reconfigurable machine tools. For illustration and validation, the methodology has been applied to an illustrative design problem. The results demonstrate the applicability and validity of the proposed methodology. The design methodology proposed in this thesis consists of three stages: module identification, module selection, and module layout synthesis. Module identification is to collect module components (building blocks) for the reconfigurable machine tool to be designed. In particular, the module identification stage involves two steps: decomposition of the existing machine tools into module components and establishment of a module component library as the selection space for the module selection stage. Module selection is to evaluate the selected module components and select an optimal set of module components for the reconfigurable machine tool design. The module selection stage involves two steps: determination of the functional requirements required to manufacture a target part family and selection of module components from the module component library. Module layout synthesis is to generate reconfigurable machine tool architecture solutions (which are represented by module layouts) and select the desired architecture solutions according to a set of criteria. The module layout synthesis stage involves two steps: module layout generation and module layout selection. In module layout generation, the methods for generalized layout enumeration, generalized layout enumeration are presented. In module layout selection, reconfigurability, structural stiffness, and error sensitivity are treated as three module evaluation criteria and selection is performed based on the indices of those criteria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".