基于BLTT-FT模型的织物撕裂性能测试过程的趋势分析和预测
Qingchun Jiao1, Yifan Zhang1, Yang Lu2
1School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, China.
PloS one
|December 1, 2025
概括
这项研究引入了一种用于实时布料撕裂性能预测的新型模型,增强了布料耐用性测试. 该模型准确预测实验趋势,提高效率和优化测试过程.
科学领域:
- 材料科学 材料科学 材料科学
- 织工程 织工程 织工程
- 数据科学数据科学数据科学
背景情况:
- 织物撕裂性能对于评估织品耐用性至关重要.
- 由于复杂的数据动态,对织物撕裂性能测试的实时预测具有挑战性.
- 现有的方法在捕捉短期细节和长期趋势方面可能缺乏准确性.
研究的目的:
- 为实时预测面料撕裂性能测试开发一个强大的模型.
- 从实验数据中有效提取关键特征,以便准确预测趋势.
- 构建适用于不同弹性等级的织物预测模型.
主要方法:
- 提出了一个混合模型,集成双向长期短期存储器 (BiLSTM),变压器编码和时间卷积网络 (TCN) 层.
- 采用"双向长期和短期注意力机制"进行趋势预测 (BLTT-FT模型).
- 利用差异分析 (ANOVA) 来验证不同织物弹性数据集的模型性能.
主要成果:
- 该模型在多步预测中实现了高精度,平均MAPE为3.06%和R2为0.9572.
- 在所有预测步骤中 (p < 0.001) 显示出明显低的误差指标 (MAE,RMSE,MAPE).
- 废弃实验证实了多模块分层方法在单步细节和多步远距离准确性方面的有效性.
结论:
- 开发的BLTT-FT模型为织物撕裂性能测试提供了出色的实时趋势预测.
- 该模型的性能在各种织物弹性等级中是一致的.
- 这种方法对提高实验效率和优化织品测试过程具有重要价值.
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