Date of Award
Mechanical, Automotive, and Materials Engineering
Green, Daniel (Mechanical, Automotive and Materials Engineering)
CC BY-NC-ND 4.0
An investigation of non-linear multi-objective optimization is conducted in order to define a set of process parameters (i.e. load paths) for defect-free tube hydroforming. A generalized forming severity indicator that combines both the conventional forming limit diagram (FLD) and the forming limit stress diagram (FLSD) was adopted to detect excessive thinning, necking/splitting and wrinkling in the numerical simulation of formed parts. In order to rapidly explore and capture the Pareto frontier for multiple objectives, two optimization strategies were developed: normal boundary intersection (NBI) and multi-objective genetic algorithm (MOGA) based on the concept of "dominated solutions". The NBI method produced a uniformly distributed set of solutions. For the MOGA method, a stochastic Kriging model was used as a surrogate model. Furthermore, the MOGA constraint-handling technique was improved, Kriging model updating was automated and a hybrid global-local search was implemented in order to rapidly explore the Pareto frontier. Both piece-wise linear and pulsating pressure paths were investigated for several case studies, including straight tube, pre-bent tube and industrial tube hydroforming. For straight tube hydroforming, the optimal load path was obtained using the NBI method and it showed a smaller corner radius compared to that predicted by the commercial program LS-OPT4.0. Moreover, the hybrid method coupling global search (MOGA) and local search (sequential quadratic programming: SQP) was applied for straight tube hydroforming, and the results showed a significant improvement in terms of the stress safety margin and reduced local thinning. For a commercial refrigerator door handle, the MOGA method was utilized to inversely analyze the loading path and the calculated path correlated well with the production path. For a hydroformed T-shaped tubular part, the amplitude and frequency of the pulsating pressure were optimized with MOGA. Thinning was reduced by 25% compared with experimental results. A multi-stage (prebent) tube hydroforming simulation was performed and it indicated that the reduction in formability due to bending can be largely compensated by end feeding the tube during hydroforming. The loading path optimized by MOGA showed that the expansion into the corner of the hydroforming die increased by 16.7% compared to the maximum expansion obtained during experimental trials.
An, Honggang, "Multi-objective Optimization of Tube Hydroforming Using Hybrid Global and Local Search" (2010). Electronic Theses and Dissertations. 454.