The use of function-driven shape definition method and GRA–EMM-based Taguchi method to multi-criterion blade preform optimization

For a blade with complex shape, preform design plays an important part in the quality of an achieved part in the forging process. So many optimization methods were developed in many researches to obtain an optimal preform. However, in these researches, they lack accuracy and efficiency in defining preform shape, and there is no systematical strategy of implementing multi-criterion optimization processes. In order to overcome these defects, a function-driven shape definition method was proposed in this paper. This method can transform geometrical shape into numerical variables which define geometrical shape easily and can be used in mathematical functions in such methods as Taguchi method and response surface method. Besides, a novel Taguchi method coupling with gray relational analysis (GRA) and entropy measurement method (EMM) was developed, which provides a systematic way of optimizing preforms from the perspective of multi-criterion. In this method, GRA transforms multiple objectives into gray relational grade and EMM determines the weights of objectives. Finally, these two methods were applied in blade preform optimization to validate their effectiveness and efficiency. The results showed that the optimized blade was decreased by 16.6% in strain variance and by 20.2% in total load.