Grey Wolf Optimization for Service Life Maximization of Natural Fiber-Reinforced Transtibial Prosthetic Sockets

Abstract

This paper presents a data-driven design for transtibial prosthetic sockets fabricated from natural fiberreinforced composites, aiming to extend their service life. An analytical model was established using experimental data from real medical prostheses to accurately capture the interplay between material properties, user activity profiles, and socket degradation mechanisms. The design parameters, identified through sensitivity analysis as most influential on the end-of-life behavior, were modeled under inherent uncertainties and constrained within practical upper and lower bounds. The optimization objective was defined as maximizing the service life of the prosthetic socket. To address this challenge, the Grey Wolf Optimization (GWO) algorithm was applied, owing to its recognized efficiency and strong convergence characteristics in solving complex benchmark problems. The results confirm that GWO provides rapid convergence and identifies optimal parameter sets within the defined search space, leading to improved life-cycle predictions. These findings demonstrate the relevance of nature-inspired metaheuristics in prosthetic design and offer a promising pathway for developing sustainable and high-performance socket solutions. The study establishes a preliminary framework that will be expanded in ongoing work to incorporate more advanced uncertainty quantification.