Innovations in the Design of Pleated Clothing Using Digital Printing and Dyeing Technology
Bibliographic record
Abstract
Pleating technology plays a major role in the design of pleated clothing. Also known as wrinkle clothing design, pleating is a high temperature process that has been significantly influenced by progress in science and technology. The combination of pattern design and pleating through the use of digital printing and dyeing technology has become a key aspect of the visual presentation, embodiment of style, and decorative art associated with pleated clothing. This paper explores the historical background and performance of the technologies used in the pleating process, and examines the various modeling techniques that have been brought to bear. The principal innovations in the pleating process can be divided up into shaping, shrinking, and decorative technologies. The relationship between changes in pleating and clothing patterns is also discussed, together with how design thinking has approached the craft of overlaying patterns on pleated clothing. Depending on the clothing and kind of technology being used, there are three main ways in which innovative design can be applied to the pleating process: to create geometric clothing patterns; to create basic clothing patterns; and through the superposition of shrinking technology. A Chinese design case is presented to illustrate how the integration of technology and art can be realized. This paper may serve as a source of future reference for technological innovation in the design of pleated clothing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".