Abstract: In the fixturing processing with the multiple clamps, the different fixturing parameters, including the fixturing sequence, the magnitude of clamping force, the locator position, and so forth, can cause the different fixturing deformation of the thin-walled workpiece. Deformation law of workpiece which is caused by a single fixturing parameter can be obtained by finite element method. However, if multiple fixturing parameters are synchronously considered, finite element method is difficult in revealing the relationship between the fixturing parameters and fixturing deformation of workpiece. Therefore, the finite element model of the multi-fixturing layout is above all established for the thin-walled workpiece. Fixturing deformations can be analyzed to be the training samples of neural network. And then, with the nonlinear mapping of neural network, the prediction model of fixturing deformation is suggested according to the training samples. Finally, the optimal model of multi-fixturing layout with the objective of minimizing the minimum fixturing deformation is presented. According to the maximum fixturing deformation of each generation, the fitness of the individual is defined to develop the genetic algorithm so that the optimal model can be solved to obtain the fixturing sequence and the locator position. The prediction model is able to?predict experimental results?within a 3%?error margin as well as predict simulated results within a 3%?error margin. The presented “analysis-prediction-control” method of fixturing deformation can not only improve the calculation efficiency of fixturing deformation, but also provide a basic theory of multi-fixturing layout design for the thin-walled workpiece.

Key words: fixturing deformation, genetic algorithm, neural network, thin-walled workpiece