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Review
. 2024 Oct 13;17(20):5009.
doi: 10.3390/ma17205009.

A Systematic Review of AI-Driven Prediction of Fabric Properties and Handfeel

Yi-Fan Tu  1 , Mei-Ying Kwan  1 , Kit-Lun Yick  1
Affiliations
Review

A Systematic Review of AI-Driven Prediction of Fabric Properties and Handfeel

Yi-Fan Tu et al. Materials (Basel). .

Abstract

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.

Keywords: AI in textiles; fabric handfeel prediction; tactile simulation; textile property prediction.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Visual flow of research process.
Figure 2
Figure 2
Illustration of the literature search process. The dark grey outer box represents the initial filtering by topic categories and year of publication, followed by the application of specific keywords within these limits (inner box).
Figure 3
Figure 3
PRISMA flow diagram of literature review [13].
Figure 4
Figure 4
Number of related articles published year by year.
Figure 5
Figure 5
Visual representation of key research topics in AI-driven fabric property prediction using VOSviewer (version 1.6.20). The source data for this visualization were obtained from the Web of Science database.
Figure 6
Figure 6
Overview of primary identified uses for AI in predicting fabric handfeel.
See this image and copyright information in PMC

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