Analysis of size dependent earing evolution in micro deep drawing of TWIP steel by using crystal plasticity modeling

Abstract

As a promising microforming process, micro deep drawing has attracted great attention for manufacturing of microparts with unique cup-features. However, the occurrence of size effect (SE) and the intrinsic characteristics of crystallographic orientation can lead to an inevitable and uncertain plastic anisotropy during the micro deep drawing and further result in the formation of earing. In this research, a size dependent polycrystalline hardening model using crystal plasticity (CP) theory considering the interactions between slip and twinning was developed via incorporating the size scaling factor into the surface layer model to represent and model the SE and further analyze the entire micro deep drawing process of the TWIP steel. Then the micro deep drawing of polycrystalline TWIP steel with different grain sizes and thicknesses was conducted using the elaborately designed tooling sets. The size dependent crystal plasticity-based finite element modeling in conjunction with a virtual polycrystalline microstructure was employed to study the earing evolution induced by grain size, orientation and geometrical size. It is revealed that an obvious earing profile occurred in the workpiece with the larger size scaling factor. In addition, it is noted that the earing at micro scale is obviously higher than that at macro scale. The asymmetric distribution of earing profile is induced by the Goss and Brass orientations, while the Cubic type orientation leads to the symmetric distribution of earing profile. This issue is mainly attributed to the symmetric distribution of slip activities than that of twinning activities, since the activated quantity of twinning volume fraction is much smaller. Therefore, the proposed size dependent polycrystalline hardening modeling is efficient in predicting the earing formation at micro scale. The study thus provides an in-depth understanding of micro deep drawing of sheet metals and accurate prediction of shape microforming of microparts.