A comparative study on the identification methods for calibration of the orthotropic yield surface and its effect on the sheet metal forming simulations
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引用次数: 0
Abstract
The predictive capability of an anisotropic yield function highly relies upon the number of the model parameters and its calibration type. Conventional calibration of a plane stress anisotropic yield function considers material behavior in uniaxial and equi-biaxial stress states, whereas it violates shear and plane strain loading conditions. In this study, the direction of the plastic flow in both loading regions was corrected by including shear and plane strain constraint terms to the conventional calibration of the Yld2000 function, and its effect on the sheet metal forming simulations, namely cup drawing and hole expansion tests, was investigated. Two highly anisotropic sheet materials (AA2090-T3 and low-carbon steel) were selected for the investigation, and the anisotropy coefficients were determined. Stress anisotropy was accurately predicted by the conventional method, whereas any decrease in the prediction of the deformation anisotropy could not occur by the applying of the constrained methods. Significant increases in the predicted cup height and differences in the number of the ears were observed by shear constraint identification in the cup drawing. The maximum thinning location in the hole expansion test could be accurately predicted by plane strain constraint identification.
期刊介绍:
Archive of Applied Mechanics serves as a platform to communicate original research of scholarly value in all branches of theoretical and applied mechanics, i.e., in solid and fluid mechanics, dynamics and vibrations. It focuses on continuum mechanics in general, structural mechanics, biomechanics, micro- and nano-mechanics as well as hydrodynamics. In particular, the following topics are emphasised: thermodynamics of materials, material modeling, multi-physics, mechanical properties of materials, homogenisation, phase transitions, fracture and damage mechanics, vibration, wave propagation experimental mechanics as well as machine learning techniques in the context of applied mechanics.