A digital twin framework development for apparel manufacturing industry
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 apparel manufacturing industry faces challenges due to fast-changing fashion trends, increased product variety, and personalized demands. Quick and optimized decision-making is crucial to overcome these barriers. This study aims to develop a methodology for applying Digital Twin (DT) technology in apparel manufacturing plants. We demonstrate the applicability and exhibit efficacy of the proposed method by presenting a case study on implementing the DT technology at a sewing assembly line. The proposed approach provides step-by-step guidance, collecting real-time data and conducting dynamic simulations to reduce bottleneck operations. DT technology assists in decision-making, enabling apparel manufacturing plants to respond quickly to changing demands and to reduce bottleneck operations. This study demonstrates the effectiveness of the proposed methodology by reducing downtime and improving production efficiency. The proposed method can improve production efficiency, reduce downtime, and respond quickly to changing demands in the apparel manufacturing industry.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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