Accurate Prediction of Springback in Forming of BIW Parts

Abstract:

The use of High Strength Steels (HSS) for automotive parts improves car performance in terms of structural strength and weight reduction. However it poses major challenges to manufacturing since HSS is prone to springback. Springback causes deviation in part geometry from its intended design thus giving problem to its subsequent assembly process. In this paper, three models for predicting springback were evaluated. First model is based on the Multiple Regression (MR) technique. Second model utilized Hill Orthotropic constitutive material model and the last model employed a neural network predictive model. All the models were evaluated by using tool surface and stamped part historical data that are obtained from three selected springback prone automotive BIW parts representing three different levels of springback severity namely high, medium and small. The results on the low springback part show that the neural network model outperforms the other approaches.

• Periodical: Applied Mechanics and Materials (Volume 165)
• Pages: 93-97
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.165.93
• Online since: April 2012
• Authors: Nagur Aziz Kamal Bashah, Ahmad Zakaria, Khairul Za’im Kamarulzaman, Achmed Mobin, Mohd Safuan Mohd Abdul Lazat, Norhamidi Muhamad, Baba M. Deros
• Keywords: Multiple Regression, Neural Network (NN), Springback

References

[1] A. Behrouzi, et al., A One Step Analytical Approach for Springback Compensation In Channel forming Process , Procs. Of the World Congress on Engineering Vol II WCE 2009, London, UK. (2009).

[2] M.F. Alfaradi, L. Xiaoxing, Determination of Springback in Metal forming, The Annals of Dunarea De Jos University of Galati Fascicle V Technologies in Machine Building ISSN 1221-4566 (2009).

DOI: 10.35219/tmb

[3] Narayanasamy & Padmanabhan , Comparison of Regression and Artificial Network Model for the Prediction of Springback during Air Bending Process of Interstitial Steel Sheet", Journal Of Intelligent Manufacturing, (2010).

DOI: 10.1007/s10845-009-0375-6

[4] Asgari et al., Statistical Analysis of Finite Element Modeling in Sheet Metal Forming and Springback Analysis, Journal of Materials Processing Technology 203 (2008), pp.129-136.

DOI: 10.1016/j.jmatprotec.2007.09.073

[5] I. Burchitz, Improvement of Springback Prediction in Sheet Metal Forming, , PhD Thesis, Netherlands Institute for Metals Research, (2008).

[6] T. Shindo, et al., Springback Simulation and Compensation for High Strength Parts Using JSTAMP , AIP Conference Proceedings 1383 (2011), pp.1086-1091.

DOI: 10.1063/1.3623724

[7] Kessler et al., Springback Simulation with Complex Hardening Material Models, LS-DYNA Anwenderforum, Bamberg (2008).

[8] S. Keeler, Tooling Technology: The Science of Forming, General Techniques to Minimize Springback, Tooling Technology-Metal Forming Magazine, (2008) pp.48-49.

[9] RADIOSS THEORY MANUAL, 10. 0 version – September 2009, Large Displacement Finite Element Analysis PART 2.

[10] K.B. Nagur Aziz, A. Zakaria, K. Khairul Za'im , M. Achmed and M.A.L. Mohd Safuan, A Shop Floor Approach to Springback Prediction, The 8th NUMISHEET Conference & Workshop, American Institute of Physics Conference Proceedings, vol. 1383, (2011).

DOI: 10.1063/1.3623656