LAUSR

This article develops an optimal design method with multiple stages for a flexure mechanism. This mechanism can be employed as a rotary joint or a torsional spring. The developed method is a hybridization of density-based topology, multilayer perceptron, and water cycle-moth flame algorithm. Firstly, the topology design for flexure mechanism is conducted to create a draft shape of flexure mechanism, and it is redesigned to seek a proper structure. Secondly, datasets of finite element simulations are collected. These data are then normalized, and the feed-forward multilayer perceptron (FMLP) is utilized to formulate the regression models for all performances. In order to determine the best suitable parameters of each FMLP model, their architectures are optimized by the Taguchi technique. By evaluating the measurement indexes such as correlation coefficient, mean square error, and root mean square error, the developed FMLP models show a superiority as compared to the multiple linear regression. Then, the sensitivity of design parameters is investigated. Thirdly, based on the well-established FMLP models, the size optimization is conducted by the water cycle-moth flame algorithm. Finally, in comparison with the FMLP-based differential evolution, FMLP-based firefly algorithm, FMLP-based particle swarm optimization, and FMLP-based teaching-learning based optimization, the present method has a better efficiency through the Friedman and Wilcoxon tests.