Abstract
Compliant mechanism has some advantages and has been widely applied in many accurate positioning systems. However, modeling the compliant mechanism behavior has suffered from many challenges, such as unstable results, and the limitation of training data set. In the field of compliant mechanism modeling, there has been no research interested in applying meta-heuristics optimization algorithms to optimize the weights and biases of the neural network globally. Additionally, the Physics-Guided Artificial Neural Network, a research direction that has received much attention recently, has not been considered in problems related to compliant mechanisms. In order to surmount those drawbacks, this paper pioneers a new approach to model behaviors of a compliant mechanism using the Hunger Game Search and the Physics-Guided Artificial Neural Network. The Hunger Game Search can directly search the model's weights and biases so that the target function that takes advantages of both physical and data information can be minimized. The investigations on diverse training set ratios and the ANOVA tests at the [Formula: see text] significance level reveal that using the Hunger Game Search can result in smaller errors than using the backpropagation method. Furthermore, applying the Hunger Game Search to a Physics-Guided Artificial Neural Network not only can reduce error but also can increase convergence speed compared to applying Hunger Game Search to conventional neural networks. Those results demonstrate the potential of the proposed method in modeling the behavior of compliant mechanisms.