An Active Transfer Learning (ATL) Framework for Smart Manufacturing with Limited Data: Case Study on Material Transfer in Composites Processing
Bibliographic record
Abstract
Unprecedented advances in Machine Learning (ML), cloud computing and sensory technology promise to enable the manufacturing industry to respond rapidly to changes in marked needs while maintaining product quality and minimizing costs. Despite the unparalleled advantages that ML offers, critical limiting factors have prevented the exhaustive cultivation of ML in advanced manufacturing. Constant shifts in the process configuration and lack of sufficient fully-descriptive data restrict the performance of predictive ML models. This paper proposes to partly address these shortfalls with an active transfer learning (ATL) model that is applied to an aerospace composites manufacturing case study. The proposed ATL framework requires 1) developing an AI-based optimal experimental design using Active Learning (AL) to maximize the information gain from the limited number of allowable manufacturing trials, and 2) equipping the manufacturing process with a robust Transfer Learning (TL) model that is trained on limited available data and is immune to shifts in the process settings. The results suggest that uncertainty-based AL approaches can significantly decrease the dependency on large datasets for obtaining accurate process models. Furthermore, in comparison with traditional TL approaches, the proposed framework represents a practical solution to further reduce the necessity for generating expensive data in advanced manufacturing applications for developing reliable and transferable predictive models.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".