Various methods exist to model leaf springs with each method having different advantages and disadvantages. Computational efficiency and accuracy are two important factors when choosing a modeling technique especially when the simulation models become large. This paper compares the computational efficiency and accuracy of an elasto-plastic leaf spring model to a neural network leaf spring model. The advantages and disadvantages of the two modeling techniques are discussed and suggestions are made for the best use of both models. It is also shown that combining the two methods can overcome some of the disadvantages and exploit the advantages of both methods. The results from the comparison quantify the accuracy and computational efficiency of the two methods giving concrete parameters for the analyst to decide on which model to use.

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