Abstract:Cross-sectional deformation of double-ridged rectangular tube (DRRT) inevitably occurs due to the inhomogeneous deformation induced by external boundary conditions in rotary draw bending (RDB). Unreasonable factor combination would aggravate the cross-sectional deformation of DRRT. So, a powerful and efficient method combining Response Surface Methodology (RSM) and Non-Sorted Genetic Algorithm II (NSGA-II) was proposed to optimize the factors to control the cross-sectional deformation of DRRT in RDB. Firstly, an orthogonal experiment was used to screen out the important factors. It was obtained that three factors-clearance between DRRT and mandrel, clearance between DRRT and bending die, and boosting of pressure die-have an important influence on the cross-sectional deformation of DRRT. It can also be observed that the variation trend of flange sagging (FS) is always consistent with that of space deformation between ridges (SDR) with the changing of factors. RSM based on a Box-Behnken design was then used to establish response surface models. The proposed response surface models were used to analyze the relationship of the important parameters to the responses, such as space deformation between ridges, and width deformation of outer and inner ridge grooves (WDO and WDI). Finally, multi-objective parameter optimization for the cross-sectional deformation of DRRT in RDB was performed by using the established model and NSGA-II algorithm. The interaction of responses was revealed and the value range of each response in the space of Pareto optimal solutions was determined. It can be observed that there is always an evident conflict between SDR and WDO in the space of Pareto optimal solutions. By using this optimization method, the absolute values of SDR and WDI were significantly reduced-by 13.17% and 17.97%, respectively-compared with those before optimization, while WDO just increase only a little.
This paper presents a self-organized fuzzy neural network (SOFNN) surface reconstruction algorithm suitable for point clouds without normal. It overcomes the defect of traditional Delaunay triangulation which is difficult to reconstruct point clouds with noises and implicit function which is limited to the number of point clouds and point clouds are required very strict. The SOFNN is based on the fuzzy clustering method optimizing training data before learning fuzzy rules, in order to remove noise data and resolve conflicts in data. The approach not only reduce computational burden of neural network, but also make it easy to fit the surface for point clouds without normal and suitable for mass point clouds. The feature of the SOFNN has dynamic self-organized structure, fast learning speed and flexibility in learning. The experiment results show that is very fine.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.