Study on multilevel optimization strategy of carbon fiber-reinforced polymer seatback and seat pan

Carbon fiber-reinforced polymer (CFRP) has garnered extensive scholarly attention owing to its remarkable mechanical properties and inherent lightweight nature. However, there remains a need for a straightforward and effective optimization approach for designing CFRP automotive components. Hence, this study introduces the CFRP multilevel optimization strategy, which is applied to the optimization design of the CFRP seatback and seat pan. Firstly, the accuracy of the two selected finite element models is validated through physical experiments. On this basis, CFRP is employed as a substitute for the original steel seatback and seat pan. Secondly, two typical dynamic working conditions are transformed into static ones, enabling the application of the ply optimization. The ply angle, shape, thickness, and stacking sequence are determined through the process of free size optimization, size optimization, and ply stacking sequence optimization. Subsequently, a reliability optimization method is established, incorporating Optimal Latin Hypercube Sampling, adaptive Kriging surrogate model, Monte Carlo Simulation, Non-dominated Sorting Genetic Algorithm-II, Entropy Weighting Method, and Modified Visekriterijumsko KOmpromisno Rangiranje. This method is applied to the reliability design of both the seatback and seat pan. Lastly, a comprehensive comparative analysis of various optimization schemes shows that, despite a slight increase in mass, reliability optimization significantly improves the reliability indices compared to ply optimization. Additionally, compared to the original steel seat frame, the reliability-optimized CFRP seatback and seat pan achieve a 31.59% reduction in mass while preserving reliability, dummy injury, and comfort measures. Hence, the CFRP multilevel optimization strategy proposed in this paper performs well in terms of both accuracy and effectiveness, providing a dependable point of reference for related CFRP optimization.