Artificial Neural Network for the Prediction of Fatigue Life of Microscale Single-Crystal Copper

Abstract

Microscale single-crystal copper is widely used in electronics, communications and other fields due to its excellent properties such as high ductility, high toughness and good conductivity. Therefore, it is particularly important to research its fatigue life. In order to explore the influence of size effect, loading frequency and shear strain on the main slip surface on the fatigue life of microscale single-crystal copper based on in situ fatigue experimental data of microscale single-crystal copper, this paper used a BP neural network algorithm to construct a single-crystal copper fatigue life prediction network model. The data set included 14 groups of training data, with 11 groups as training sets and 3 groups as testing sets. The input characteristics were length, width, height, loading frequency and shear strain of the main sliding plane of a microscale single-crystal copper sample. The output characteristic was the fatigue life of microscale single-crystal copper. After training, the mean square error (MSE) of the model was 0.03, the absolute value error (MAE) was 0.125, and the correlation coefficient (R2) was 0.93271, indicating that the BP neural network algorithm can effectively predict the fatigue life of microscale single-crystal copper and has good generalization ability. This model can not only save the experimental time of fatigue life measurement of micro-scale single-crystal copper, but also optimize the properties of the material by taking equidistant points in the range of characteristic parameters. Therefore, the current study demonstrates an applicable and efficient methodology to evaluate the fatigue life of microscale materials in industrial applications.