Deep drawing simulation of Tailored Blanks

Tailored blanks are increasingly used in the automotive industry. A tailored blank consists of different metal parts, which are joined by a welding process. These metal parts usually have different material properties. Hence, the main advantage of using a tailored blank is to provide the right material properties at specific parts of the blank. The movement of the weld during forming is extremely important. Unwanted weld displacement can cause damage to both the product and the tool. This depends mainly on the original weld position and the process parameters. However experimental determination of the optimum weld position is quite expensive. Therefore a numerical tool has been developed for simulations of tailored blank forming. The Finite Element Code Dieka is used for the deep drawing simulations of some geometrically simple products. The results have been validated by comparing them with experimental data and show a satisfactory correlation

Warm Deep Drawing of Aluminium Sheet

Aluminium sheet drawing processes can be improved by manipulating local flow behaviour\ud by means of elevated temperatures and temperature gradients in the tooling. Forming tests\ud showed that a substantial improvement is possible not only for 5xxx but also for 6xxx series\ud alloys. Finite element method simulations can be a powerful tool for the design of warm\ud forming processes and tooling. Their accuracy will depend on the availability of materials\ud models that are capable of describing the influence of temperature and strain rate on the flow\ud stresses. Two models, an adapted Nadai power law and a dislocation based Bergström type\ud model, are compared by means of simulations of a cup drawing process. Experimental\ud drawing test data are used to validate the modelling approaches, whereas the model parameters\ud follow from tensile tests

Deep drawing simulations of Tailored Blanks and experimental verification

Tailored Blanks are increasingly used in the automotive industry.\ud A combination of different materials, thickness, and coatings can be welded\ud together to form a blank for stamping car body panels. The main advantage\ud of using Tailored Blanks is to have specific characteristics at particular parts\ud of the blank in order to reduce the material weight and costs.\ud To investigate the behaviour of Tailored Blanks during deep drawing, the\ud finite element code DiekA is used. In this paper, simulations of the deep\ud drawing of two products using Tailored Blanks are discussed. For\ud verification, the two products are stamped to gain experimental information.\ud The correlation between the experimental results and the simulation results\ud appears to be satisfactory

Compensation of deep drawing tools for springback and tool-deformation

Manual tool reworking is one of the most time-consuming stages in the\ud preparation of a deep drawing process. Finite Elements (FE) analyses are now widely\ud applied to test the feasibility of the forming process, and with the increasing accuracy of the\ud results, even the springback of a blank can be predicted. In this paper, the results of an FE\ud analysis are used to carry out tool compensation for both springback and tool/press\ud deformations. Especially when high-strength steels are used, or when large body panels are\ud produced, tool compensation in the digital domain helps to reduce work and save time in the\ud press workshop. A successful compensation depends on accurate and efficient FE-prediction,\ud as well as a flexible and process-oriented compensation algorithm. This paper is divided in\ud two sections. The first section deals with efficient modeling of tool/press deformations, but\ud does not discuss compensation. The second section is focused on springback, but here the\ud focus is on the compensation algorithm instead of the springback phenomenon itself

Equivalent drawbead performance in deep drawing simulations

Drawbeads are applied in the deep drawing process to improve the control of the material flow\ud during the forming operation. In simulations of the deep drawing process these drawbeads can be replaced by\ud an equivalent drawbead model. In this paper the usage of an equivalent drawbead model in the finite element\ud code DiekA is described. The input for this equivalent drawbead model is served by experiments or by a 2D\ud plane strain drawbead simulation. Simulations and experiments of the deep drawing of a rectangular product\ud are performed to test the equivalent drawbead model performance. The overall conclusion reads that a real\ud drawbead geometry can succesfully be replaced by the equivalent drawbead mode

The breakage prediction for hydromechanical deep drawing based on local bifurcation theory

A criterion of sheet metal localized necking under plane stress was established based on the bifurcation theory and the characteristics theory of differential equation. In order to be capable to incorporate the directional dependence of the plastic strain rate on stress rate, Ito-Goya’s constitutive equation which gave a one to one relationship between stress rate component and plastic strain rate component was employed. The hydromechanical deep drawing process of a cylindrical cup part was simulated using the commercial software ABAQUS IMPLICIT. The onset of breakage of the part during the forming process was predicted by combining the simulation results with the local necking criterion. The proposed method is applied to the hydro-mechanical deep drawing process for A2219 aluminum alloy sheet metal to predict the breakage of the cylindrical cup part. The proposed method can be applied to the prediction of breakage in the forming of the automotive bodies

Deep Learning Face Representation by Joint Identification-Verification

The key challenge of face recognition is to develop effective feature representations for reducing intra-personal variations while enlarging inter-personal differences. In this paper, we show that it can be well solved with deep learning and using both face identification and verification signals as supervision. The Deep IDentification-verification features (DeepID2) are learned with carefully designed deep convolutional networks. The face identification task increases the inter-personal variations by drawing DeepID2 extracted from different identities apart, while the face verification task reduces the intra-personal variations by pulling DeepID2 extracted from the same identity together, both of which are essential to face recognition. The learned DeepID2 features can be well generalized to new identities unseen in the training data. On the challenging LFW dataset, 99.15% face verification accuracy is achieved. Compared with the best deep learning result on LFW, the error rate has been significantly reduced by 67%