Data-augmented prediction of laser shock peening residual stress fields via attention-integrated generative adversarial networks

Accurately and efficiently predicting residual stress fields after laser shock peening (LSP) is critical for optimizing material performance and extending component service life. Traditional finite element simulations, however, are computationally heavy and time-consuming, limiting their use in iterative design and real-time optimization. This paper proposes an innovative method integrating finite element simulation, generative modeling, and attention-enhanced learning for rapid residual stress field prediction, with key results: A conventional attention-enhanced graph convolutional generative adversarial network model (AGCGAN) is designed to predict residual stress fields from LSP parameters, boosting accuracy and cross-domain generalization. A finite element platform based on a specific material constitutive model is developed to implement data augmentation, generating enriched training data under varying laser energy and spot overlap rates to ensure physical authenticity. The proposed neural network addresses limited LSP simulation samples and training difficulties through effective utilization of augmented data, achieving 95.98 % prediction accuracy, 445x faster computation than traditional methods, and highly realistic generated images, efficiently solving design challenges in LSP surface treatment at low cost.