A Systematic Review of AI-Driven Prediction of Fabric Properties and Handfeel Article Swipe
Yifan Tu
,
Mei-Ying Kwan
,
Kit‐Lun Yick
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/ma17205009
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/ma17205009
Artificial intelligence (AI) is revolutionizing the textile industry by improving the prediction of fabric properties and handfeel, which are essential for assessing textile quality and performance. However, the practical application and translation of AI-predicted results into real-world textile production remain unclear, posing challenges for widespread adoption. This paper systematically reviews AI-driven techniques for predicting these characteristics by focusing on model mechanisms, dataset diversity, and prediction accuracy. Among 899 papers initially identified, 39 were selected for in-depth analysis through both bibliometric and content analysis. The review categorizes and evaluates various AI approaches, including machine learning, deep learning, and hybrid models, across different types of fabric. Despite significant advances, challenges remain, such as ensuring model generalization and managing complex fabric behavior. Future research should focus on developing more robust models, integrating sustainability, and refining feature extraction techniques. This review highlights the critical gaps in the literature and provides practical insights to enhance AI-driven prediction of fabric properties, thus guiding future textile innovations.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.3390/ma17205009
- https://www.mdpi.com/1996-1944/17/20/5009/pdf?version=1728818199
- OA Status
- gold
- Cited By
- 6
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403398380
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4403398380Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/ma17205009Digital Object Identifier
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A Systematic Review of AI-Driven Prediction of Fabric Properties and HandfeelWork title
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-13Full publication date if available
- Authors
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Yifan Tu, Mei-Ying Kwan, Kit‐Lun YickList of authors in order
- Landing page
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https://doi.org/10.3390/ma17205009Publisher landing page
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https://www.mdpi.com/1996-1944/17/20/5009/pdf?version=1728818199Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1996-1944/17/20/5009/pdf?version=1728818199Direct OA link when available
- Concepts
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Artificial intelligence, Computer science, Textile, Machine learning, Quality (philosophy), Generalization, Sustainability, Predictive modelling, Data science, Materials science, Mathematics, Ecology, Epistemology, Biology, Composite material, Philosophy, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 6Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4365151331, https://openalex.org/W6853703773, https://openalex.org/W6687527504, https://openalex.org/W4312613142, https://openalex.org/W4400927023, https://openalex.org/W4394567325, https://openalex.org/W4387907064, https://openalex.org/W609105960, https://openalex.org/W2768894717, https://openalex.org/W4390482129, https://openalex.org/W3118615836, https://openalex.org/W2150220236, https://openalex.org/W4310375528, https://openalex.org/W2092519067, https://openalex.org/W4392921768, https://openalex.org/W2725430179, https://openalex.org/W4321092750, https://openalex.org/W4391104291, https://openalex.org/W4386993310, https://openalex.org/W4366261674, https://openalex.org/W4311805755, https://openalex.org/W3165156775, https://openalex.org/W2178622571, https://openalex.org/W2547678650, https://openalex.org/W2331145029, https://openalex.org/W2911419090, https://openalex.org/W6758806874, https://openalex.org/W2774737136, https://openalex.org/W2188203292, https://openalex.org/W4237112279, https://openalex.org/W6842247780, https://openalex.org/W2946313203, https://openalex.org/W4400977224, https://openalex.org/W4386068498, https://openalex.org/W2487792063, https://openalex.org/W4289638462, https://openalex.org/W2791836551, https://openalex.org/W6730470530, https://openalex.org/W2532096414, https://openalex.org/W3007698992, https://openalex.org/W2605124795, https://openalex.org/W4367842874, https://openalex.org/W2321988567, https://openalex.org/W4392263511, https://openalex.org/W4286611321, https://openalex.org/W4382173434, https://openalex.org/W4399851998, https://openalex.org/W842107886, https://openalex.org/W2787125973, https://openalex.org/W4386735683, https://openalex.org/W2007872832, https://openalex.org/W2557415428, https://openalex.org/W4381925223, https://openalex.org/W2911428727, https://openalex.org/W2188645385, https://openalex.org/W4294439855 |
| referenced_works_count | 56 |
| abstract_inverted_index.39 | 71 |
| abstract_inverted_index.AI | 89 |
| abstract_inverted_index.as | 110 |
| abstract_inverted_index.by | 8, 56 |
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| abstract_inverted_index.899 | 67 |
| abstract_inverted_index.The | 83 |
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| abstract_inverted_index.for | 20, 43, 52, 74 |
| abstract_inverted_index.the | 5, 10, 27, 138, 142 |
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| abstract_inverted_index.deep | 94 |
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| abstract_inverted_index.Among | 66 |
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| abstract_inverted_index.model | 59, 112 |
| abstract_inverted_index.paper | 47 |
| abstract_inverted_index.these | 54 |
| abstract_inverted_index.types | 101 |
| abstract_inverted_index.which | 17 |
| abstract_inverted_index.Future | 119 |
| abstract_inverted_index.across | 99 |
| abstract_inverted_index.fabric | 13, 117, 153 |
| abstract_inverted_index.future | 157 |
| abstract_inverted_index.hybrid | 97 |
| abstract_inverted_index.papers | 68 |
| abstract_inverted_index.posing | 41 |
| abstract_inverted_index.remain | 39 |
| abstract_inverted_index.review | 84, 136 |
| abstract_inverted_index.robust | 126 |
| abstract_inverted_index.should | 121 |
| abstract_inverted_index.Despite | 104 |
| abstract_inverted_index.complex | 116 |
| abstract_inverted_index.content | 81 |
| abstract_inverted_index.dataset | 61 |
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| abstract_inverted_index.models, | 98, 127 |
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| abstract_inverted_index.remain, | 108 |
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| abstract_inverted_index.textile | 6, 22, 37, 158 |
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| abstract_inverted_index.However, | 26 |
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| abstract_inverted_index.ensuring | 111 |
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| abstract_inverted_index.unclear, | 40 |
| abstract_inverted_index.AI-driven | 50, 150 |
| abstract_inverted_index.accuracy. | 65 |
| abstract_inverted_index.adoption. | 45 |
| abstract_inverted_index.advances, | 106 |
| abstract_inverted_index.analysis. | 82 |
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| abstract_inverted_index.essential | 19 |
| abstract_inverted_index.evaluates | 87 |
| abstract_inverted_index.handfeel, | 16 |
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| abstract_inverted_index.techniques. | 134 |
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| abstract_inverted_index.bibliometric | 79 |
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| corresponding_author_ids | https://openalex.org/A5018378567 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I14243506 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.6000000238418579 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.79304036 |
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| citation_normalized_percentile.is_in_top_10_percent | False |