Influence mechanism of process parameters on axial compressive response of <scp>CNTs</scp> reinforced filament‐wound riser with metal liner

Abstract Carbon nanotubes (CNTs) reinforced filament‐wound riser with metal liner face critical challenges in maintaining structural integrity under axial compressive loads, especially when subjected to the harsh conditions of deep‐sea environments. However, existing manufacturing processes for filament winding risers lack a comprehensive understanding of how key process parameters interact to influence axial compressive strength. This study aims to deeply explore the influence mechanism of process parameters on the axial compressive strength of CNTs reinforced filament‐wound riser with metal liner. The compressive strength model was constructed by using the multivariate nonlinear method, and the effectiveness of the model was ensured through verification. Meanwhile, the influence mechanism of a single process parameter was analyzed to reveal the internal relationship between the winding tension, feed rate, curing time, and the maximum axial compressive load. The coupling effects of process parameters were analyzed to elucidate the mechanism by which parameter interactions govern compression performance. Furthermore, the sensitivity analysis was carried out to determine the key sensitive parameters, divide the process window, and optimize the parameters. Finally, the optimal process parameters combination with the maximum axial compressive load of 88.4074 kN, 51.7448 N of winding tension, 14.8607 m/min of winding speed, and 6.3741 h of curing time was determined based on the PSO‐GWO algorithm. The research results can provide a novel approach for the manufacturing of high‐performance deep‐sea risers. Highlights Novel approach integrating multiple nonlinear regression and global–local sensitivity analysis. Stable region division method was adopted to determine the optimal ranges for composite wound risers. PSO‐GWO hybrid algorithm was employed to optimize the CNTs reinforced composite process parameters.