Abstract
Residual stresses in metallic components greatly affect the geometrical accuracy of advanced high-strength steels (AHSS) products used for automotive industry. Chain-die forming is a promising method to fabricate AHSS products which can minimize the residual stress induced. This paper aims to understand the residual stress in chain-die-formed dual-phase (DP) steels through analytical and numerical modelling together with experimental validation. Analytical and numerical models were firstly employed to predict the stress development and associated residual stresses induced by chain-die forming in consideration of an as-received residual stress field and a comprehensive material-hardening model. The residual stresses in different regions of formed parts were practically measured using the neutron diffraction method to verify the developed models. Same as the mesh size of the examining points of finite element analysis (FEA) models, the averaged over the same sampling size is measured to guarantee the accuracy. It seems to be consistent with the simulation and experimental results in consideration of both factors. However, compared with as-received residual stress, the kinematic factor has a more dominating impact on the final residual stress. The results presented should contribute to the prediction of geometric errors and minimize product defects caused by the residual stresses in chain-die forming or equivalent sheet metal-forming industry.
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Acknowledgments
The authors acknowledge Baosteel Co. Ltd. for providing the material properties of the experimental samples. This project is financially supported by Baosteel-Australia Joint Research and Development Centre (BAJC) BA17013 and ANSTO Bragg Institute beam time proposal 4865 and 6499. The authors also would like to acknowledge the assistance of Emeritus Professor J.L. Duncan, Mr Zhen Qian, Mr Chang Wang and Mr Kang An in writing this paper.
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Sun, Y., Luzin, V., Khan, S. et al. Understanding of residual stresses in chain-die-formed dual-phase (DP) metallic components: predictive modelling and experimental validation. Int J Adv Manuf Technol 103, 3337–3360 (2019). https://doi.org/10.1007/s00170-019-03671-9
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DOI: https://doi.org/10.1007/s00170-019-03671-9