Type synthesis of reconfigurable composite joints based on motion decomposition and reconstruction
Bibliographic record
Abstract
To enrich the types of Reconfigurable Composite Joints (RCJs), this paper focuses on the general synthesis method for the design of RCJs based on motion decomposition and reconstruction (MDR). The overall concept of MDR targeting the design of RCJs is first introduced. Then, the concept and its application are described, including six steps: ( i ) determination of the main chain (MC); ( ii ) motion modeling of the MC; ( iii ) analysis and decomposition of the total motion of the MC; ( iv ) reconstruction of the chains with different submotions by means of auxiliary chains (ACs); ( v ) determination of the pertinent adjustable chain, and assembly of the MC, ACs, and the adjustable chain; and ( vi ) layout of the actuation scheme. Subsequently, three kinds of RCJs with different main chains are generated systematically by means of the concepts proposed herein. Finally, one reconfigurable parallel-kinematics machine (PKM) with one proposed RCJ in a limb is used as an example, which offers applications in the design of reconfigurable mechanisms. The proposed concept, MDR, is not only suitable for type synthesis of simple kinematic chains, but also potentially applicable to reconfigurable kinematic chains.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 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".