Analysis of behavior of helical, disk and tape spring types in space conditions

The subject of helical springs, which are vital and widely used components in various industries, particularly gains significantly more importance in the aerospace industry. Helical springs are utilized for energy storage purposes in various applications, notably in spacecraft separator mechanisms. However, one of the major challenges in designing these components is reducing their mass, especially in space applications where every gram carries substantial significance. This paper delves into optimizing the mass of helical springs used in spacecraft separator mechanisms. Initially, we solve the mass optimization problem of helical springs using conventional mathematical equations and the genetic algorithm. Subsequently, we design a new model for the springs using artificial neural networks and conduct optimization using this new model. The obtained results demonstrate that using the proposed artificial neural network model offers considerable advantages over the initial method. This optimization using the proposed model enhances the accuracy of helical spring designs. Numerical results indicate that the output in the neural network related to shear stress is 88.545, differing by 0.83% from the finite element method, and the output related to deformation in the neural network is 41.76, differing by 0.58% from the finite element method. These findings underscore the significantly improved performance of the proposed model compared to previous methods.