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Springback and sidewall curl prediction in U-bending process of AHSSthrough finite element method and artificial neural network approach |
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| รหัสดีโอไอ | |
| Creator | 1. Natchanun Angsuseranee 2. Ganwarich Pluphrach 3. Bhadpiroon Watcharasresomroeng 4. Arkhom Songkroh |
| Title | Springback and sidewall curl prediction in U-bending process of AHSSthrough finite element method and artificial neural network approach |
| Publisher | Research and Development Office, Prince of Songkla University |
| Publication Year | 2018 |
| Journal Title | Songklanakarin Journal of Science and Technology |
| Journal Vol. | 40 |
| Journal No. | 3 |
| Page no. | 534 |
| Keyword | springback, U-bending, AHSS, FEM, ANN |
| URL Website | http://rdo.psu.ac.th/sjstweb/index.php |
| ISSN | 0125-3395 |
| Abstract | Advanced high strength steels (AHSS) have been used extensively in the automotive industry to reduce weight and fuelconsumption. However, increasing the strength of a material leads to the reduction in formability and a high degree ofspringback. Moreover, sidewall curl has been detected from U-bending operations of AHSS which caused problems in theassembly line. The aim of this research is to compare the efficiency of springback and sidewall curl prediction of AHSS gradeSPFC980Y in the U-bending process by the finite element method and artificial neural network approach. Input data for theprediction consisted of punch radius (Rp), die radius (Rd), and blank holder force (Fb). The back propagation neural networkmodel was trained by the springback values from a U-bending die experiment with 27 conditions. Efficiency estimations ofspringback and sidewall curl prediction were considered from the root mean square error (RMSE). The results showed that thefinite element method was more efficient than the artificial neural network approach. The RMSE values from the finite elementmethod for springback and sidewall curl were 0.104 and 0.092, respectively. |
