Forming of near-net-shaped and load-adapted functional components, as it is developed in theTransregionalCollaborative Research Centreon Sheet-Bulk Metal FormingSFB/TR73, causes different problems, which lead to non-optimal manufacturing results. For these high complex processes the prediction of forming effects can only be realized by simulations. A stamping process of pressing eight punches into a circular blank is chosen for the considered investigations. This reference process is designed to reflect the main aspects, which strongly affect the final outcome of forming processes. These are the orthotropic material behaviour, the optimal design of the initial blank and the influences of different contact and friction laws. The aim of this work is to verify the results of finite element computations for the proposed forming process by experiments. Evaluation methods are presented to detect the influence of the anisotropy and also to quantify the optimal blank design, which is determined by inverse form finding. The manufacturing accuracy of the die plate and the corresponding roughness data of the milled surface are analysed, whereas metrological investigations are required. This is accomplished by the help of advanced measurement techniques like a multi-sensor fringe projection system and a white light interferometer. Regarding the geometry of the punches, micromilling of the die plate is also a real challenge, especially due to the hardness of the high-speed steelASP2023(approx. 63 HRC). The surface roughness of the workpiece before and after the forming process is evaluated to gain auxiliary data for enhancing the friction modelling and to characterise the contact behaviour.
Biaxial tensile testing of sheet metals is becoming increasingly popular for sheet metal forming. Determining equivalent stresses in biaxial tensile specimens is more complicated than in conventional uniaxial tensile specimens. In the present study, we compare four different approaches to calculate effective stresses during biaxial tensile loading of a cruciform specimen: (a) partial unloading method, where stresses are determined based on force–strain curves; (b) identification with uniaxial tensile testing; (c) an analysis of equivalent biaxial tests; and (d) numerical simulations. Considering experimental results for an AA1050 aluminium alloy and for a low‐carbon steel DC06, we show that, for the cruciform sample studied here, two methods do not yield physically reasonable results: The uniaxial approach does not properly take into account the effect of transverse loading, and the equivalent biaxial approach exhibits uncertainties in strain measurement data. The most comprehensible approach is the numerical method, because it also yields detailed information about the local stress and strain states. The numerical results are in excellent agreement with the partial unloading method in terms of the initial flow stress and of effective stress–strain curves for strains up to 0.02, with both methods predicting a similar effective cross section of 18.0 mm2 for the considered specimen.
In the context of metal forming, optimization issues typically lead to inverse problems with a least-squares minimization as the objective. Due to the nonlinearities in forming simulations, iterative optimization approaches have to be considered. Gradient-based solution strategies for two inverse problems are proposed and compared to each other. Firstly, the identification of elasto-plastic material parameters is regarded. Secondly, a recently developed approach for the determination of an optimal workpiece design is investigated. As a special feature, both approaches can be coupled in a non-invasive fashion to arbitrary external finite element software via subroutines.
In this contribution the Finite Element Model Updating (FEMU) approach is utilized to determine the material parameters of sheet-steel. From experimental testing it is observed that the considered cold-rolled steel exhibits orthotropic behaviour. To regard this in the simulation, a user-implemented material model based on a quadratic yield function is used. Via the method of digital image correlation (DIC), the displacement field of a biaxially loaded specimen is measured from images taken at different stages of loading. Comparing the experimentally determined displacements to those obtained by simulation an error measure is defined which can be minimized by optimization algorithms. Starting with initial values for the orthotropic elasto-plastic material parameters, the FEM model is thus updated consecutively until a specified error margin is reached.
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