Enhancing Stress-Strain Predictions with Seq2Seq and Cross-Attention based on Small Punch Test

TL;DR

This paper presents a deep learning method combining GAF transformation, Seq2Seq modeling, and cross-attention to accurately predict stress-strain curves of steels from small punch test data, improving traditional methods.

Contribution

It introduces a novel deep learning framework that transforms load-displacement data into images and uses advanced attention mechanisms for improved stress-strain prediction accuracy.

Findings

Achieves minimum mean absolute error of 0.15 MPa

Maximum mean absolute error of 5.58 MPa

Demonstrates superior accuracy over traditional techniques

Abstract

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.