SUBSTRUCTURE COUPLING WITH JOINT IDENFICATION FOR RECONFIGURABLE MANUFACTURING SYSTEMS
Bibliographic record
Abstract
Today’s manufacturing industries require rapid production of small batch sized products with great accuracy and productivity. When compared with traditional dedicated systems, reconfigurable manufacturing systems (RMS) offer rapid change in system configurations, machines and controls to adjust to flexible demands. This can be achieved by combining modular substructures together with different functionalities depending on requirements. In this study, a method of assembling the known dynamics of substructures is investigated through the receptance coupling approach. The classical receptance technique is enhanced by identifying the joint dynamics between substructures through experimental and finite element (FE) analyses. This identification method also includes the translational and rotational degrees of freedom responses, which represent the mass, spring, and damping elements of the joint. The determination of rotational responses can be very challenging, and the proposed method solves the rotational responses with two separate experimental measurements using gauge tools. This novel identification method overcomes the limitations posed by other identification methods, by minimizing numerical errors and problems associated with convergence. Experimental tests, using a fastener joint, were performed to verify the effectiveness of the joint identification method.
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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) | 0.000 | 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 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".