An adaptive recognition method for reliable collaboration of manufacturing services based on edge-aggregated graph convolutional network
Bibliographic record
Abstract
Current methods for assessing the reliability of Manufacturing Service Collaboration Chain (MSCC) primarily rely on service quality metrics and numerical calculations. However, these traditional methods face significant challenges in practical applications. This is due to idealised assumptions inherent in the definition of reliability, the often-low quality of historical data, and factors such as the coupling of service physical attributes, the flexibility of collaboration patterns, and the dynamic changes in MSCC topology. To overcome the aforementioned problems and enhance the accuracy and comprehensiveness of MSCC reliability assessment, this paper proposes an Reliable Collaboration Adaptive Recognition Network (RCEN). The RCEN introduces an improved graph convolutional neural network architecture, integrating a deep feature extraction mechanism with an optimised node information aggregation strategy. This method fully leverages the advantages of graph data in modelling complex service dependencies. It constructs a dynamic MSCC graph based on high-frequency historical operational data from the manufacturing process, where nodes and edges characterise the physical and operational attributes of manufacturing shop-floor services, respectively. Specifically, the graph construction method captures the inherent service collaboration patterns within manufacturing workflows. Combined with domain-specific quality metrics, it exhibits strong generality and scalability. Experimental results on multiple benchmark datasets demonstrate that the proposed method significantly outperforms three representative state-of-the-art methods in the task of MSCC reliability identification.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".