DA-DONN: A data-augmented and dual-optimized neural network method for fatigue crack growth rate prediction

Abstract

Accurate prediction of fatigue crack growth rate (FCGR) is of great significance in the field of materials science and engineering. To address the limitations of existing prediction methods due to data scarcity and suboptimal model performance, this paper proposes a method called the Data-Augmented and Dual-Optimized Neural Network (DA-DONN). The method employs piecewise cubic Hermite interpolation (PCHIP) to augment the FCGR data, thereby alleviating the small-sample problem. Bayesian optimization (BO) is used to tune the hyperparameters of the neural network, and the horned lizard optimization algorithm (HLOA) is applied to optimize the initial weights and biases, thus improving the prediction accuracy. Experimental results indicate that DA-DONN achieves a significantly lower MSE than SA-DNN on the 7075 aluminum alloy test set. On the 6013 aluminum alloy dataset, DA-DONN also outperforms DLFCO-DNN and MFA-DNN in terms of MAE, RMSE, and Mean RE, demonstrating its superior accuracy and practical feasibility.