Document Type : Research Paper


Department of Mechanical Engineering, Higher Institute of Technological Studies of Sfax, ISET de Sfax, Tunisia


Bending is one of the most frequently used processes in the sheet metal products industry. The major users are mainly the automotive, aeronautics and electrical engineering industries. It is necessarily a cold forming operation of a flat material, with or without lubricant, obtained notably by exceeding its elastic limit. After retraction of the tools and relaxation of the stresses, a springback consequently occurs and a permanent deformation persists causing certain geometric modifications of the product. As a matter of fact, this phenomenon, will absolutely affect the angle and curvature of the bend, for such reason it must be taken into consideration in order to manufacture sheet metal parts bent within acceptable tolerance limits. However, the value of this springback is influenced by a multiplicity of process parameters, such as the thickness of the sheet, the hold time of the bending operation, the material properties and last but not least the depth of strike of the tool. In this paper, we have developed a model for predicting springback in the air V-bending process using the design of experiments method. Four three-level factors were considered in order to model springback in using the response surface method (RSM). The experimental tests were carefully carried out on a HACO press brake and on aluminum, ordinary steel and stainless steel specimens with different thicknesses. The in-depth study of the response surfaces to the different tests with the method of analysis of variance (ANOVA), allowed us to determine a robust empirical model linking the springback to the variables of the study. In addition, several relevant numerical simulations using the finite element method (FEM) with software (Abaqus) were performed to predict the evolution of springback when varying the parameters in the field of design of experiments. In fact, the comparison of the values predicted by the two approaches shows a satisfactory agreement.


Main Subjects

  • Gedekar, R. D., Kulkarni, S. R., & Kavad, M. B. (2008). Optimization of input process parameters affecting on springback effect in sheet metal ‘V’bending process for CR2 grade steel sheet of IS 513-2008 material by using taguchi method. International research journal of engineering and technology (IRJET), 5(7), 381-386.
  • Leu, D. K. (2019). Relationship between mechanical properties and geometric parameters to limitation condition of springback based on springback–radius concept in V-die bending process. The international journal of advanced manufacturing technology101(1), 913-926.
  • Wang, J., Verma, S., Alexander, R., & Gau, J. T. (2008). Springback control of sheet metal air bending process. Journal of manufacturing processes10(1), 21-27.
  • Bouhêlier, C. (1982). Sheet metal working, Heavy plate forming, Ed. Techniques engineer, B7 630. (In French).
  • Gandhi, A. H., & Raval, H. K. (2008). Analytical and empirical modeling of top roller position for three-roller cylindrical bending of plates and its experimental verification. Journal of materials processing technology197(1-3), 268-278.
  • Trzepiecinski, T., & Lemu, H. G. (2017). Prediction of springback in V-die air bending process by using finite element method. MATEC web of conferences(Vol. 121, p. 03023). EDP Sciences.
  • Tao, J., Fu, Z., & Gao, H. (2019). Air bending of sheet metal based on the grey prediction model. Journal of engineering science & technology review12(3)., 123-129. Doi:25103/jestr.123.17
  • Aerens, R., Vorkov, V., & Duflou, J. R. (2019). Physics of large radius air bending. Procedia manufacturing29, 161-168.
  • Prastyo, Y., Adhi Yatma, W., & Hernadewita, H. (2018). Reduction bottle cost of Milkuat LAB 70 ml using optimal parameter setting with Taguchi method. Journal of applied research on industrial engineering5(3), 223-238.DOI: 22105/JARIE.2018.138366.1041
  • Khalili, S., & Mosadegh Khah, M. (2020). A new queuing-based mathematical model for hotel capacity planning: a genetic algorithm solution. Journal of applied research on industrial engineering7(3), 203-220. DOI:22105/JARIE.2020.244708.1187
  • Onyekwere, O. S., Oladeinde, M. H., & Uyanga, K. A. (2020). Optimization of parameter settings to achieve improved tensile and impact strength of bamboo fibre composites. Journal of applied research on industrial engineering7(4), 344-364. DOI: 22105/JARIE.2020.257974.1207
  • Meslameni, W., & Kamoun, T. (2021). Detection of an imbalance fault by vibration monitoring: case of a screw compressor. Journal of applied research on industrial engineering8(1), 27-39.DOI: 22105/JARIE.2021.269384.1243
  • Satpute, S.A., & Chopade, R.P. (2018). Experimental study on spring back phenomenon in sheet metal V- die bending, International research journal of engineering and technology, 5(6), 2323-2326.
  • Özdemir, M. (2020). Optimization of spring back in air V bending processing using taguchi and RSM method. Mechanics26(1), 73-81.
  • Ben Ali, R.O.A., & Chatti, S. (2019). Modeling springback of thick sandwich panel using RSM, The international journal of advanced manufacturing technology, 103, 3375–3387.
  • Serban, F. M., Grozav, S., Ceclan, V., & Turcu, A. (2020). Artificial neural networks model for springback prediction in the bending operations. Tehnički vjesnik27(3), 868-873.
  • Guo, Z., & Tang, W. (2017). Bending angle prediction model based on BPNN-spline in air bending springback process. Mathematical problems in engineering2017.
  • Aerens, R., Vorkov, V., & Duflou, J. R. (2019). Springback prediction and elasticity modulus variation. Procedia manufacturing29, 185-192.
  • Wasif, M., Iqbal, S. A., Tufail, M., & Karim, H. (2020). Experimental analysis and prediction of springback in v-bending process of high-tensile strength steels. Transactions of the indian institute of metals73(2), 285-300.
  • Karaağaç, İ. (2017). The evaluation of process parameters on springback in V-bending using the flexforming process. Materials research20, 1291-1299.
  • Vuong, G. H., & Nguyen, D. T. (2020). Studies on predicting spring-back and verifying the effects of temperature, sheet thickness and punch speed on forming force of v-bending for ss400 steel plate. In Parinov I., Chang SH., Long B. (Eds.), Advanced materials(pp. 97-108). Cham:
  • Aerens, R., Vorkov, V., & Duflou, J. R. (2019). Springback prediction and elasticity modulus variation. Procedia manufacturing29, 185-192.
  • Miranda, S. S., Barbosa, M. R., Santos, A. D., Pacheco, J. B., & Amaral, R. L. (2018). Forming and springback prediction in press brake air bending combining finite element analysis and neural networks. The journal of strain analysis for engineering design53(8), 584-601.