Evaluation Method and Application of Assembly Yield in Two-Dimension Multi-Station Assembly Processes Based on Number-Theoretical Net

Method for calculating assembly yield in two-dimension multi-station assembly processes is developed based on Number-Theoretical Net (NT-net). The discrepancy of NT-net is analyzed, and the principle of generating good lattice point (glp) based on NT-net method is introducted. Afterwards, taking fixture locating variations which are sampled using NT-net method for input vectors, the samples are substituted into state space model of dimension variation propagation in multi-station assembly processes to get output vectors. The statistics for qualified sample is accomplished, after comparing output vectors with the variations of measuring points on component. Assembly yield in two-dimension multi-station assembly processes is gained when qualified sample divided by total sample. Finally, a real case in automotive body floor assembly is given as an example to calculate the assembly yield in two-dimension three-station assembly processes. The result is validated by using Monte carlo simulation. It provides a new way to predict assembly yield in two-dimension multi-station assembly processes.

Method for Estimating Failure Probability of Mechanical Structures Using Number Theoretical Net (NT-net) Simulation

According to poor computational accuracy at small to median sample sizes of Monte Carlo ( MC ) simulation techniques in estimating the probability failure of mechanical structures, the number theoretical net ( NT-net ) simulation method is proposed to reduce computing effort. Several key concepts, such as good point set, good-lattice point ( glp ), discrepancy and NT-net method, are defined. The sampling stategy is improved by introducing NT-net that can provide better convergent rate over MC. The new method is used to estimate failure probability of the side impact bar on the car door. Results indicate the computational effort needed by NT-net for the same accuracy is about 1/12 of that needed by the MC-based method, and the obtained results are more stable.