Publication: Springback and sidewall curl prediction in U-bending process of AHSS through finite element method and artificial neural network approach
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Issued Date
2018
Resource Type
File Type
application/pdf
ISSN
1253395
Other identifier(s)
2-s2.0-85053709368
Rights Holder(s)
Scopus
Bibliographic Citation
Songklanakarin Journal of Science and Technology. Vol 40, No.3 (2018), p.534-539
Suggested Citation
Angsuseranee N., Pluphrach G., Watcharasresomroeng B., Songkroh A. Springback and sidewall curl prediction in U-bending process of AHSS through finite element method and artificial neural network approach. Songklanakarin Journal of Science and Technology. Vol 40, No.3 (2018), p.534-539. doi:10.14456/sjst-psu.2018.87 Retrieved from: https://hdl.handle.net/20.500.14740/4950
Abstract
Advanced high strength steels (AHSS) have been used extensively in the automotive industry to reduce weight and fuel consumption. However, increasing the strength of a material leads to the reduction in formability and a high degree of springback. Moreover, sidewall curl has been detected from U-bending operations of AHSS which caused problems in the assembly line. The aim of this research is to compare the efficiency of springback and sidewall curl prediction of AHSS grade SPFC980Y in the U-bending process by the finite element method and artificial neural network approach. Input data for the prediction consisted of punch radius (Rp), die radius (Rd), and blank holder force (Fb). The back propagation neural network model was trained by the springback values from a U-bending die experiment with 27 conditions. Efficiency estimations of springback and sidewall curl prediction were considered from the root mean square error (RMSE). The results showed that the finite element method was more efficient than the artificial neural network approach. The RMSE values from the finite element method for springback and sidewall curl were 0.104 and 0.092, respectively. © 2018, Prince of Songkla University. All rights reserved.