Springback analysis in air bending process through experiment based artificial neural networks

Senol, Ozgu
Esat, Volkan
Darendeliler, Haluk
Sheet metal bending is one of the most frequently used sheet metal forming processes in manufacturing industry. This study investigates bending parameters and springback phenomenon of a stainless-steel sheet in air bending process. In most of the applications, springback is determined either by trial and error procedures or by using numerical methods. Artificial Neural Network (ANN) approach has proved to be a helpful tool for the engineers. ANN is used in this study to predict the springback amounts of stainless steel sheets through experiment based networks. Air bending process is first modeled and analyzed by a commercial finite element code. Springback amounts for different sheet thicknesses and bend angles are computed. In addition to computational modeling, experimentation of the air bending processes is carried out and experimental results are used in artificial neural network development to show the feasibility of ANN based on experimentation. Experimental outcome is also used for validation of the FE analysis of the process, which demonstrates good agreement. It is observed that ANN can be applied effectively to determine springback in air bending process, which embodies significant potential to determine air bending process parameters for industrial applications such as punch stroke.


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Springback prediction is vital in order to obtain the desired part shape in metal forming processes. In most of the applications, springback amount is determined by trial and error procedures, and recently by using numerical methods or through handbook tables. Artificial Neural Network (ANN) is a helpful tool for the engineers and applied in this study to determine the springback amounts in air, V-die and wipe bending processes. For this purpose, bending processes are analyzed by commercial finite element (...
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Citation Formats
O. Senol, V. Esat, and H. Darendeliler, “Springback analysis in air bending process through experiment based artificial neural networks,” 2014, vol. 81, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/32911.