Enhancing Stress-Strain Predictions with Seq2Seq and Cross-Attention based on Small Punch Test
It addresses the problem of accurate and efficient stress-strain prediction for materials scientists, offering an alternative to traditional experimental methods, though it appears incremental as it builds on existing Seq2Seq and attention techniques.
This paper tackles predicting true stress-strain curves for high-strength steels from small punch test data using a deep-learning approach, achieving mean absolute errors between 0.15 MPa and 5.58 MPa.
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.