Prediction of welded joint fatigue properties based on a novel hybrid SPDTRS-CS-ANN method

This paper proposes a prediction model of welded joint fatigue properties based on single -parameter decision-theoretic rough set (SPDTRS)-cuckoo search (CS)-artificial neural network (ANN) hybrid algorithm. To establish the fatigue performance database of EH36 steel, the pre-processing and data cleaning are carried out by analytic hierarchy process (AHP) and box-plot method to obtain reliable fatigue properties prediction. Therein, the SPDTRS theory is used to analyze the weight of fatigue properties influencing factors, the CS algorithm is used to avoid the over-fitting and local optimization of ANN. During process, the influencing factors are regarded as input and the material related parameters C and m are conducted as output to realize the fatigue properties prediction, which improves the accuracy and stability of the present prediction method. According to the comparisons between the experimental and predicted results, it is found that the predicted S-N curves are within +/- 1.1 error band of the experimental results, the average error of fatigue life is less than 10%, and can be within +/- 1.2 error band. As a result, the fatigue properties prediction model reasonably shows the fatigue properties of welded structures, and provides a certain reference for fatigue design of welded structures.

Automated summary

This paper proposes a prediction model for the fatigue properties of welded joints, using a hybrid algorithm combining single-parameter decision-theoretic rough set (SPDTRS), cuckoo search (CS), and artificial neural network (ANN). The model improves the accuracy and stability of fatigue properties prediction by analyzing the weight of influencing factors and avoiding overfitting and local optimization. The experimental results show that the predicted S-N curves and fatigue life have a small error compared to the actual results, demonstrating the model's reliability and potential for fatigue design of welded structures.