Optimal Engineering System Design Guided by Data-Mining Methods

An optimal engineering design problem is challenging because nonlinear objective functions usually need to be evaluated in a high-dimensional design space. This article presents a data-mining–aided optimal design method, that is able to find a competitive design solution with a relatively low computational cost. The method consists of four components: (1) a uniform-coverage selection method, that chooses design representatives from among a large number of original design alternatives for a nonrectangular design space; (2) feature functions, of which evaluation is computationally economical as the surrogate for the design objective function; (3) a clustering method, that generates a design library based on the evaluation of feature functions instead of an objective function; and (4) a classification method to create the design selection rules, eventually leading us to a competitive design. Those components are implemented to facilitate the optimal fixture layout design in a multistation panel assembly process. The benefit of the data-mining–aided optimal design is clearly demonstrated by comparison with both local optimization methods (e.g., simplex search) and random search-based optimizations (e.g., simulated annealing). KEY WORDS: Classification and regression tree; Fixture layout optimization; K-means clustering; Kriging model; Multistation assembly processes; Uniform design