Prediction and design optimization of mechanical properties for rubber fertilizer hose reinforced with helically wrapped nylon

Abstract

Predicting and optimizing the mechanical performance of the helically wound nylon-reinforced rubber fertilizer hose (HWNR hose) is crucial for enhancing the performance of hose pumps. This study aims to enhance the service life of HWNR hoses and the efficiency of liquid fertilizer transport. First, a finite element simulation model and a mathematical model were established to analyze the influence of fiber layer arrangement on the maximum shear strain on the coaxial surface (MSS) and the reaction force on the extrusion roller (RF). For the first time, the Crested Porcupine Optimizer algorithm was used to improve the Generalized Regression Neural Network (CPO-GRNN) method to establish a surrogate model for predicting the mechanical properties of HWNR hoses, and it was compared with Response Surface Methodology (RSM). Results showed CPO-GRNN's superiority in handling complex nonlinear problems. Finally, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was employed for optimization design. Compared to the original HWNR hose with an MSS of 0.906 and an RF of 30,376N, the optimized design reduced the MSS by 7.99% and increased the RF by 2.46%, significantly enhancing their service life and liquid fertilizer transport capacity. However, further research on fatigue damage is needed.