Version 1
: Received: 18 September 2024 / Approved: 19 September 2024 / Online: 19 September 2024 (15:54:50 CEST)
How to cite:
Tu, Y.-F.; Kwan, M.-Y.; Yick, K.-L. A Systematic Review of AI-Driven Prediction of Fabric Properties and Handfeel. Preprints2024, 2024091549. https://doi.org/10.20944/preprints202409.1549.v1
Tu, Y.-F.; Kwan, M.-Y.; Yick, K.-L. A Systematic Review of AI-Driven Prediction of Fabric Properties and Handfeel. Preprints 2024, 2024091549. https://doi.org/10.20944/preprints202409.1549.v1
Tu, Y.-F.; Kwan, M.-Y.; Yick, K.-L. A Systematic Review of AI-Driven Prediction of Fabric Properties and Handfeel. Preprints2024, 2024091549. https://doi.org/10.20944/preprints202409.1549.v1
APA Style
Tu, Y. F., Kwan, M. Y., & Yick, K. L. (2024). A Systematic Review of AI-Driven Prediction of Fabric Properties and Handfeel. Preprints. https://doi.org/10.20944/preprints202409.1549.v1
Chicago/Turabian Style
Tu, Y., Mei-ying Kwan and Kit-lun Yick. 2024 "A Systematic Review of AI-Driven Prediction of Fabric Properties and Handfeel" Preprints. https://doi.org/10.20944/preprints202409.1549.v1
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 811 papers initially identified, 26 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
fabric handfeel prediction; AI in textiles; textile property prediction; tactile simulation
Subject
Chemistry and Materials Science, Materials Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
