Relevance vector machine and fuzzy system based multi-objective dynamic design optimization: A case study

To improve the original design flaws of overturning assembly of glass stacking machine taken as a case study, a multi-objective optimization approach integrated relevance vector machines (RVM), multi-objective genetic algorithms (MOGA) and fuzzy system are presented for the optimal dynamic design problem. Firstly, the multi-objectives of the overturning assembly are constructed by the use of dynamic structure optimization design theory. The motion simulation and finite element analysis of overturning assembly are utilized for sampling scheme given by uniform design to collect the train dataset. The dataset could describe the non-linear behaviors of dynamic and static characteristics of variety of mechanical structures, which is identified by RVMs. Sequentially, RVM- based meta-model as fitness function is combined with MOGA to obtain the Pareto optimal set. Finally, a fuzzy inference system is established as decision-making support to obtain the optimum preference solution. Therefore, the modified physical prototype with the round solution proofed feasibility and efficiency of this approach.