Optimizing the profile of spiral corrugated and Corrugated polyethylene pipes using neural networks

The aim of this paper is to obtain an optimized profile for the manufacturing of a thermoplastic PE80 pipe to achieve the lowest possible weight per pipe unit while maintaining an allowable amount of diameter variation according to ISO 9969 standard. In this paper, the behavior of the material will be analyzed using the FEM (Finite Element Analysis) method in Ansys Mechanical software. The types of pipes studied (in terms of geometry) are corrugated and spiral corrugated pipes, and the goal is to obtain an optimized profile with varying geometric conditions that results in the least weight while adhering to the constraints set by the standard. To precisely find the optimized profile, a very high number of runs from the dataset is required, which would be costly; therefore, in this paper, with a selection of initial runs chosen by the experimental design method, a neural network is trained for the weight function so that it can be directly studied as a function. This way, instead of adjusting and solving the Ansys model each time to extract the weight of the dataset or the deformation under loading, the function can be executed in a fraction of a second, providing the values instantly. The optimization algorithm used is a genetic algorithm, which utilizes the developed neural network functions and obtains the optimal weight conditions under the deformation constraints of the standard.