Model naming.

Fabric tearing performance testing experiment is an important part of evaluating fabric durability. The aim of this paper is to solve the problem of real-time prediction of fabric tearing performance testing by effectively extracting key features from experimental data and constructing a prediction model applicable to the process of fabric tearing performance testing. In this study, the trend prediction model for the experimental process of fabric tear performance testing (BLTT-FT) based on the “bidirectional long- and short-term attention mechanism” is adopted. A prediction model combining the improved Bi-directional Long Short-Term Memory (BiLSTM) structure, Transformer encoding layer, and Temporal Convolutional Network (TCN) layer is proposed. While considering sequence information globally, the model captures the bidirectional dependence of time series, reduces model complexity through the TCN layer, and finally optimizes prediction accuracy via the fully connected layer and activation function, thus achieving multi-step prediction. Analysis of variance (ANOVA) indicates that, across multiple datasets constructed from fabrics with different elasticity grades, the model shows extremely significant differences (p 2) of multi-step prediction is as high as 0.9572. The ablation experiments confirm that multi-modular hierarchical modeling effectively solves the problem of detail accuracy of single-step prediction and long-range dependence of multi-step prediction. The results show that the proposed model performs well in real-time trend prediction results for different data sets constructed from fabrics with different elasticity grades. By predicting the dynamics of the experimental process of fabric tearing performance testing in real time, this study has exploratory value in improving the experimental efficiency and optimizing the experimental process.织物撕裂性能测试实验是评估织物耐用性的重要环节。本研究旨在通过从实验数据中有效提取关键特征，并构建适用于织物撕裂性能测试流程的预测模型，解决织物撕裂性能测试的实时预测难题。本研究采用了基于双向长短时注意力机制的织物撕裂性能测试实验趋势预测模型（BLTT-FT），提出了一种融合改进双向长短时记忆网络（Bi-directional Long Short-Term Memory, BiLSTM）结构、Transformer编码层与时间卷积网络（Temporal Convolutional Network, TCN）层的预测模型。该模型在全局考量序列信息的同时，能够捕捉时间序列的双向依赖关系，并通过TCN层降低模型复杂度，最终通过全连接层与激活函数优化预测精度，从而实现多步预测。方差分析（Analysis of variance, ANOVA）结果表明，在基于不同弹性等级织物构建的多组数据集上，该模型的多步预测决定系数（R²）高达0.9572，且差异具有极显著性（p<0.001）。消融实验证实，多模块分层建模有效解决了单步预测的细节精度问题与多步预测的长程依赖问题。实验结果显示，所提模型在基于不同弹性等级织物构建的各类数据集上，均能取得优异的实时趋势预测效果。通过实时预测织物撕裂性能测试实验的动态变化，本研究对于提升实验效率、优化实验流程具有探索性价值。