Multiobjective Optimization for Fixture Locating Layout of Sheet Metal Part Using SVR and NSGA-II

Abstract

Fixture plays a significant role in determining the sheet metal part (SMP) spatial position and restraining its excessive deformation in many manufacturing operations. However, it is still a difficult task to design and optimize SMP fixture locating layout at present because there exist multiple conflicting objectives and excessive computational cost of finite element analysis (FEA) during the optimization process. To this end, a new multiobjective optimization method for SMP fixture locating layout is proposed in this paper based on the support vector regression (SVR) surrogate model and the elitist nondominated sorting genetic algorithm (NSGA-II). By using ABAQUS™ Python script interface, a parametric FEA model is established. And the fixture locating layout is treated as design variables, while the overall deformation and maximum deformation of SMP under external forces are as the multiple objective functions. First, a limited number of training and testing samples are generated by combining Latin hypercube design (LHD) with FEA. Second, two SVR prediction models corresponding to the multiple objectives are established by learning from the limited training samples and are integrated as the multiobjective optimization surrogate model. Third, NSGA-II is applied to determine the Pareto optimal solutions of SMP fixture locating layout. Finally, a multiobjective optimization for fixture locating layout of an aircraft fuselage skin case is conducted to illustrate and verify the proposed method.