Design of artificial neural network using particle swarm optimisation for automotive spring durability

This paper presents the optimisation of spring fatigue life based on an artificial neural network (ANN) architecture and particle swarm optimisation algorithm (PSO) using ISO 2631 vertical vibration as input. The road-induced vibration of a ground vehicle caused the spring to fail due to fatigue and human discomfort. Hence, there is a need to model the relationship between these two parameters for spring design assistance. Vibration and force signals were extracted from a quarter car model simulation for fatigue life and ISO 2631 vertical vibration estimations. PSO was applied to the datasets for ANN weights and biases adjustments while the mean squared error (MSE) was set as the objective function. For validation purposes, a set of independent datasets was applied to the ANN. The residuals were analysed using Lilliefors normality and error histogram. For prediction accuracy, the predicted fatigue lives were analysed using scatter band approach and compared with traditional trained ANN. The results have shown that most of the PSO-based ANN predicted fatigue lives were in the acceptable region and the root mean square error (RMSE) value of 0.6391 life cycles in natural logarithm was obtained. The PSO-based ANN has shown improved performance compared to the conventional ANN approach in predicting fatigue life.