Surrogate Modeling in Fixture Layout Design for Multi-Station Sheet Metal Assembly

摘要

Fixture layout optimization is the fundamental task of fixture design, to determine the number, type, and location of the basic fixturing elements of locators, supports, and clamps, as opposed to the detailed design of the fixture assembly. An optimal fixture layout improves the robustness of a fixture system against environmental noises, reduces product variability, and leads to manufacturing cost reduction. It is multi-variable, multi-constrain nonlinear engineering optimization. The automotive body assembly process, consisting of up to 70 stations, is a typical multi-station assembly process to fabricate the structural frame of an automobile. One of the fixture layout design challenges raised by this multi-station assembly process is a difficult and complex global optimality in which a high dimension design space will have to be explored. Consequently, it makes a global optimality more difficult and may require large-sized computing time. Approximation methods such as Design of Experiments (DOE) and response surface (RS) modeling and Design and Analysis of Computer Experiments ( DACE) are commonly used in engineering design to minimize the computational expense of running such analyses and simulations. In this paper, to address this problem effectively, a multi-station assembly variation propagation model, in which relationship between dimensional quality and different variation sources is indicated, is depicted firstly. And then we briefly review several experimental design optimality criterions such as D-op-timality and E-optimality in term of their capability to generate optimal design matrix for the complex design space. Surrogate modeling, also called metamodeling, is presented to build and explore fixture design space. With the metamodel of fixture design space, more robust and efficient solution to reduce assembly variation can be achieved.