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Record W6957726412 · doi:10.60692/6zk1a-x0828

Knitted Denim Fabrics: Fabrication Process and Fibrous Influence on Several Properties of the Fabric

2022· article· en· W6957726412 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2022
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDenimFiberPolyesterAbrasion (mechanical)YarnFabrication

Abstract

fetched live from OpenAlex

Denim-like knitted fabrics are getting popular for their several favorable properties, such as flexibility, comfort, and ease of manufacturing. This study aimed to manufacture knitted denim fabrics made from various blend ratios of cotton, polyester, and spandex fibers. Seven fabrics with a fiber blend ratio ranging from 95% cotton/5% spandex to 30% cotton/65% polyester/5% spandex were developed using a weft circular knitting machine. First, the twill effect of the knitted denim fabric was brought by following a cross terry knitting structure to produce each fabric sample of this study. After that, the fabric performance was analyzed by characterizations, such as areal density, pilling and abrasion resistance, dimensional stability, stretch, and recovery, tear strength, bursting strength, air permeability, vertical wicking, liquid absorbency capacity, and different colorfastness tests. The results showed that different cotton, polyester, and spandex fiber compositions did not significantly affect knitted denim fabrics' weight per unit area, abrasion resistance, and different color fastness properties. However, the elongation and vertical wicking test data showed that the knitted denim fabrics with a higher cotton fiber ratio were better. However, the results from shrinkage, spirality, pilling, recovery, strength, air permeability, and liquid absorbency capacity tests revealed the benefits of having a higher polyester fiber ratio in the cotton/polyester/spandex blended knitted denim fabrics. One-way analysis of the variance test was also performed on the generated data of this study and reported in the respective section of the article.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.211
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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