Lifetime optimization design of rubber V-belt based on BP-PSO algorithm

A life optimization design methodology for rubber V-belts is proposed based on the BP-PSO algorithm to enhance service life. Initially, a lifetime prediction model grounded in the Wiener process is developed, providing a solid theoretical basis, with experimental validation confirming the model's accuracy exceeds 95 percent. Subsequently, a BP neural network model is constructed using a uniform experimental design to generate training, validation, and testing datasets, thereby verifying the validity and predictive capability of the BP neural network. Finally, lifetime optimization of the rubber V-belt is conducted using the Particle Swarm Optimization algorithm. The optimal physical parameters identified include a breaking strength of 6.989 kN, reference-force elongation of 5.652 percent, elastic modulus of 72 MPa, and hardness of 78, resulting in an optimized V-belt lifetime of 276.377 hours. The optimization results demonstrate a 15.2 percent increase in V-belt lifetime compared to the pre-optimization value of 239.91 hours, indicating a significant improvement and offering v aluable theoretical guidance for the optimal design and development of rubber V-belt products within the industry.