对人工智能驱动的布料属性和手感预测进行系统审查
Yi-Fan Tu1, Mei-Ying Kwan1, Kit-Lun Yick1
1School of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong, China.
Materials (Basel, Switzerland)
|October 26, 2024
概括
人工智能 (AI) 通过分析织物特性来增强织品质量预测. 本综述探讨了人工智能模型,数据集和准确性,确定了现实世界织生产采用的挑战.
科学领域:
- 织工程 织工程 织工程
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
背景情况:
- 人工智能 (AI) 正在改变织行业,特别是在预测织物特性和手感方面,这对于质量评估至关重要.
- 尽管AI具有潜力,但实际实施和整合到现实世界织生产中面临着重大采用挑战.
研究的目的:
- 系统地审查人工智能驱动的技术来预测织物特性和手感.
- 分析AI模型机制,数据集多样性和织行业的预测准确性.
- 确定当前的挑战和未来的研究方向,用于AI在织品质量预测.
主要方法:
- 对最初识别的899篇论文进行了文献计量分析和内容分析,其中39篇被选中进行深入审查.
- 各种AI方法的分类和评估,包括机器学习,深度学习和混合模型.
- 专注于模型机制,数据集多样性和不同织物类型的预测准确性.
主要成果:
- 人工智能模型在预测织物特性和手感方面表现有希望,这对织品质量至关重要.
- 机器学习,深度学习和混合模型是主要使用的AI技术.
- 关键的挑战包括确保模型的概括性和管理复杂的织物行为.
结论:
- 人工智能在织品属性预测方面取得了重大进展,但实际采用受到一般化和复杂性问题的阻碍.
- 未来的研究应该优先考虑强大的模型开发,可持续性整合和精细的特征提取.
- 本综述为增强人工智能驱动的织物属性预测和指导未来织创新提供了关键的见解.
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