Multiobjective robust design optimization of fatigue life for a truck cab

• Dual surrogate models are presented for metamodeling the fatigue life.
• MOPSO algorithm is adopted to perform the robust optimization design of fatigue life.
• Fatigue simulation model of truck cab is modeled and experimental validation.
• A hybrid multi-criteria decision making model is implemented to find the best solution from Pareto set.

Structural optimization for vehicle fatigue durability signifies an exciting topic of research to improve its long-term safety and performance with minimum cost. Nevertheless, majority of the existing studies has been dealing with deterministic optimization and has not involved uncertainties, which could lead to an unstable or even useless design in practice. In order to simultaneously enhance the performance and robustness of the fatigue life for a truck cab, a multiobjective optimization is proposed in this study. After validating the simulation model, different dual surrogate modeling (DSM) methods are attempted to overcome the limitation of classical dual response surface (DRS) method; and subsequently the most accurate model, namely dual Kriging (DKRG) in this case, is selected through a comparative study. Then, the multiobjective particle swarm optimization (MOPSO) algorithm is adopted to perform the optimization. Compared with traditional single objective optimization strategies which yield only one specific optimum, MOPSO allows producing a set of non-dominated solutions over the entire Pareto space for a non-convex problem, which provides designers with more insightful information. Finally, a multi-criteria decision making (MCDM) model, which integrates the techniques of order preference by similarity to ideal solution (TOPSIS) with grey relation analysis (GRA), is implemented to find a best compromise optimum from the Pareto set. The selected optimum demonstrated not only to improve the fatigue life of the truck cab, but also to enable the design less sensitive to presence of uncertainties.