Configuration optimization of the feature-oriented reference system in large component assembly

Multi-station measurement is a key technology for the high accuracy and efficiency of large component assembly. The unity of measurement coordinate systems (MCSs) and assembly coordinate system (ACS) is achieved by registration of enhanced reference system (ERS) points. As the transformation errors of assembly features are related to the configuration of ERS points, and to minimize them as possible, the feature-oriented reference system is of great importance, and a configuration optimization method is proposed in this paper. The detailed analyses with regard to structures, inspections, and tolerances of assembly features are conducted, and the complex constraints are established. By applying them and considering the direct connections between MCSs, the modified configuration model is built. The proposed optimization method is based on the improved binary particle swarm optimization (IBPSO), which involves a two-stage strategy and a novel mutation. The mutation is developed by using feasible centers to attract and correct infeasible particles, and simultaneously, to maintain the particle diversity. The performed experiments show that the method can effectively output optimal positions of ERS points, and the reference system is eventually hybrid on the premise of meeting accuracy requirements. The increase of ERS points is beneficial, but no further optimization happens when the amount reaches the upper limit. The limits are 12 and 18 when the measurement instruments are located at two and three different stations respectively.