Improving numerical predictability of Springback
To reach a sufficient level of accuracy of springback prediction the following must be taken into account: • evolution of elastic properties of a material during forming; • numerical stabilization technique, variation of friction coefficient, method of unloading and number of integration points through the thickness have a significant influence on springback; • the constitutive model must be able to describe all stages of the Bauschinger effect [1]. Early reyielding may introduce plastic deformations during springback (Figure 3);
A Model of Large-Strain Cyclic Plasticity and its Application to Springback Simulation
This paper presents a framework of constitutive modeling of large-strain cyclic plasticity which describes both the deformation- and texture-induced anisotropies of materials. In this model, the transient Bauschinger effect is described accurately by a new equation of the backstress evolution, and the strain-range and mean-strain dependencies of cyclic strain hardening are expressed by a model of workhardening stagnation. For the description of texture-induced anisotropy, any of widely accepted yield criteria of orthotropic anisotropy is easily incorporated into this model. This paper demonstrates the advantage of the present model in the springback analysis over other classical models by comparing the FE simulations of draw bending with the corresponding experimental results.
A Study on Technology Embedded English Classes Using QR Codes
The development of information and technology has brought plenty of changes to the educational environment. The prevalence of smart phones is particularly playing a huge role in shaping learning methods. Smart phones provide the opportunity to scan QR codes, which can greatly ease access to information. Due to a high recognition speed, recognition rate, and restoration rate, they can be useful tools for English teachers to use in their class. In this study, we suggest using QR codes for writing and picture descriptions. Based on this study, more research should invest in Technology Embedded English teaching models to create better English classes for students.