Tu Yi-Fan, Kwan Mei-Ying, Yick Kit-Lun
School of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong, China.
Materials (Basel). 2024 Oct 13;17(20):5009. doi: 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.
人工智能（AI）正在通过改进对织物性能和手感的预测来彻底改变纺织行业，而这些对于评估纺织品质量和性能至关重要。然而，人工智能预测结果在实际纺织生产中的实际应用和转化仍不明确，这给广泛采用带来了挑战。本文通过关注模型机制、数据集多样性和预测准确性，系统地综述了用于预测这些特性的人工智能驱动技术。在最初识别的899篇论文中，通过文献计量分析和内容分析选择了39篇进行深入分析。该综述对各种人工智能方法进行了分类和评估，包括机器学习、深度学习和混合模型，涉及不同类型的织物。尽管取得了重大进展，但挑战依然存在，比如确保模型的通用性和管理复杂的织物行为。未来的研究应专注于开发更强大的模型、整合可持续性以及改进特征提取技术。本综述突出了文献中的关键差距，并提供了切实可行的见解，以加强人工智能驱动的织物性能预测，从而指导未来的纺织创新。
