Prediction of Assembly Variation During Early Design

An approach to evaluating product assembly precision considering the effect of joint surface deformation

“…Therefore, considering the assembly variation, the maximum difference between the z-coordination of point P 7.3 and the z-coordination of point P 7.4 can be calculated as: 0.0308 þ 0.0558 ¼ 0.0866, so the angle between the beam and working plane of the panel decided by the assembly features Similarly, the other key assembly precision indicator decided by F7.1 and F7.2 is concluded as n ð1Þ 2 ¼ 0:504. Therefore, the total key assembly precision indicator in assembly sequence 1 can be calculated by equation (11) as follows: n ð1Þ ¼ 1 2 P 2 j¼1 n ð1Þ j ¼ 0:504. With the same steps, the total assembly precision indicators in assembly sequence 2 can also be concluded.…”

“…Zhou et al 10 proposed an assembly sequence deviation propagation model and a quality evaluation approach based on degree of dimensional variation. Zhao et al 11 proposed an assembly variation prediction method in the early design stage, using basic geometric entities such as blocks and cylinders to simplify the detailed design geometry, and built a metamodel to depict the relationship between design and manufacturing factors such as geometry, material, assembly sequence, etc. and the product assembly precision.…”

An assembly precision prediction method for customized mechanical products based on GAN-FTL

“…Traditional product assembly precision testing requires physical prototype trial and actual assembly testing, which has the limitations of uncontrollable accuracy, delayed detection, and high cost. For this reason, many scholars have studied the prediction of product assembly precision based on digital prototypes, which is divided into three main areas: simulation analysis based on digital prototypes, tolerance analysis methods, 2,3 and finite element methods. [5][6][7][8][9][10][11] These methods can predict the assembly precision by building a numerical model of the product.…”

Variation modeling of aeronautical thin-walled structures with multi-state riveting

“…Fernlund et al developed a methodology for modeling a large complex part such as the aft strut fairing, they measured deformations for a number of parts and compared to predict deformations based on models developed using a FE based composites processing software [8]. Zhao et al developed a method to move assembly variation analysis into the early stages of aircraft development where critical partitioning, sourcing, and production decisions are often made for component parts that have not yet been designed, they used regression analysis and artificial neural networks to build variation transfer functions between design and manufacturing [9]. Most of these methods and models are aimed at parts or products themselves and solved their respective question quiet well.…”