Optimization of loading path in hydroforming of parallel double branched tube through response surface methodology. (January 2018)

• Record Type: Journal Article
• Authors: Chen, Mingtao; Xiao, Xiaoting; Tong, Jianghuai; Guo, Heng; Wen, Jianping
• Is Part Of: Advances in engineering software. Volume 115(2018)
• Page Start: 429
• Page End: 438
• Publication Date: 2018-01
• Subjects: Parallel double branched tube -- Hydroforming -- Response surface methodology -- Loading path -- Optimization; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2017.11.003
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: A combination of theory and experimental techniques, response surface methodology in combination with the finite element modeling was employed to obtain the optimal loading path in the hydroforming of the parallel double branched tube. The relationship between the design objectives and the design variables was described by the mathematical models. The multi-objectives optimization for the parallel double branched tube hydroforming was achieved by the desirability method. Abstract: In this work, the response surface methodology along with the finite element method was employed to optimize the loading path for hydroforming parallel double branched tube parts. A combination of experimental design, numerical simulations and regression analysis was utilized, to determine the mathematical models between objectives and design variables. Consequently, the effectiveness of the models was evaluated through variance analysis and was utilized to predict the behavior of the tube hydroforming process. The effects and corresponding interactions of the design variables on the parallel double branched tube hydroforming were illustrated and discussed. Moreover, the response surface methodology and desirability method were applied, in order to obtain the optimal loading path within the given optimization criteria. The optimized result was in good agreement with the experimental results.