Computer-Aided Reconfiguration Planning: An Artificial Intelligence-Based Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The manufacturing industry today faces a highly volatile market in which manufacturing systems must be capable of responding rapidly to market changes while fully exploiting existing resources. Reconfigurable manufacturing systems (RMS) are designed for this purpose and are gradually being deployed by many mid-to-large volume manufacturers. The advent of RMS has given rise to a challenging problem, namely, how to economically and efficiently reconfigure a manufacturing system and the reconfigurable hardware within it so that the system can meet new requirements. This paper presents a solution to this problem that models the reconfigurability of a RMS as a network of potential activities and configurations to which a shortest path graph-searching strategy is applied. Two approaches using the A* algorithm and a genetic algorithm are employed to perform this search for the reconfiguration plan and reconfigured system that best satisfies the new performance goals. This search engine is implemented within an AI-based computer-aided reconfiguration planning (CARP) framework, which is designed to assist manufacturing engineers in making reconfiguration planning decisions. Two planning problems serve as examples to prove the effectiveness of the CARP framework.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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 it