用小冲压试验数据预测高强度钢真实应力-应变曲线
Enhancing Stress-Strain Predictions with Seq2Seq and Cross-Attention based on Small Punch Test
- 将位移数据转为图像，用序列到序列模型加交叉注意力预测
- 误差最低0.15 MPa，最高5.58 MPa，精度优于传统方法
- 适合材料性能快速评估，尤其适用于数据少的场景
本文提出一种新型深度学习方法，基于小冲压试验（SPT）的载荷-位移数据预测高强度钢的真实应力-应变曲线。该方法利用格拉米安角场（GAF）将载荷-位移序列转换为图像，以捕捉时空特征，并采用基于LSTM的编码器-解码器架构的序列到序列（Seq2Seq）模型，通过多头交叉注意力机制提升预测精度。实验结果表明，该方法预测精度显著优于传统手段，最小平均绝对误差为0.15 MPa，最大为5.58 MPa。该方法为材料科学中的真实应力-应变关系预测提供了高效、高精度的新途径。
原文摘要 · Abstract (English)
This paper introduces a novel deep-learning approach to predict true stress-strain curves of high-strength steels from small punch test (SPT) load-displacement data. The proposed approach uses Gramian Angular Field (GAF) to transform load-displacement sequences into images, capturing spatial-temporal features and employs a Sequence-to-Sequence (Seq2Seq) model with an LSTM-based encoder-decoder architecture, enhanced by multi-head cross-attention to improved accuracy. Experimental results demonstrate that the proposed approach achieves superior prediction accuracy, with minimum and maximum mean absolute errors of 0.15 MPa and 5.58 MPa, respectively. The proposed method offers a promising alternative to traditional experimental techniques in materials science, enhancing the accuracy and efficiency of true stress-strain relationship predictions.
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