The deep drawing process is a widely used sheet metal forming process. It is frequently used in the automotive industry to manufacture products with complicated shapes and curvatures. An initially flat or pre-shaped sheet material, the blank, is clamped between a die and a blankholder. The blankholder is loaded by a blankholder force, which is necessary to prevent wrinkling and to control the material flow into the die cavity. Then the punch is pushed into the die cavity, simultaneously transferring the specific shape of the punch and the die to the blank.

An incorrect design of the tools, the blank shape or an incorrect choice of material, lubrication and process parameters can yield a product with a deviating shape or with failures. A deviating shape is caused by elastic springback after forming and retracting the tools. The most frequent types of failure are wrinkling, necking (and subsequently tearing), scratching and orange peel. Wrinkling may occur in areas with high compressive strains, necking may occur in areas with high tensile strains, scratching is caused by defects of the tool surface and orange peel may occur after excessive deformations, depending on the grain size of the material.

Without extensive knowledge of the influences of all these variables on the deep drawing process, it is hardly possible to design the tools adequately and make a proper choice of blank material and lubricant to manufacture a product with the desired shape and performance. As a result, after the first design of the tools and choice of blank material and lubricant, an extensive and time consuming trial and error process is started to determine the proper tool design and all other variables, leading to the desired product. This trial and error process can yield an unnecessary number of deep drawing strokes, or may even require redesigning the expensive tools. To reduce this waste of time and cost, process modelling for computer simulation can be used to replace the experimental trial and error process by a virtual trial and error process.

The finite element method is used worldwide to simulate the deep drawing process. For an accurate simulation of a real-life deep drawing process an accurate numerical description of the tools is necessary, as well as an accurate description of material behaviour, contact behaviour and other process variables. One of the main problems for deep drawn products is springback. Even when the process is set up carefully, the product will spring back when the tools are released. In industrial practice, deformations due to springback are compensated manually, by doing extensive measurements on prototype parts, and altering the tool geometry by hand. This is a time consuming and costly operation. The FE method is a powerful tool to show springback deformations before prototypes are made. The results of a simulation can be used to modify the tools automatically, using the results of a FE simulation. In other words, the original tools are compensated for the springback. With an effective and reliable tool, the die redesign-process can be significantly faster. But, the tool can also be used earlier in the process, integrating structural and geometrical design right from the start. The application of ‘design for manufacturing’
opens up new possibilities for creating parts with high performance materials and complex shapes.

Currently, the accuracy and reliability of numerical simulations of sheet metal forming processes do not always satisfy the industrial requirements. Therefore extensive research in the field of sheet metal forming is and will be necessary to decrease the existing gap between the real-life deepdrawing process and the predictions obtained from deep drawing simulations.

When the quality of the deep drawing simulations is good enough, simulations can be used as a tool to check the manufacturability (robustness) and geometry of the desired part. To be able to achieve an optimal process, several geometric and parameter variations must be investigated.
Therefore, many simulations must be carried out with different process parameters and with different tool geometries. Currently, this requires a lot of expensive and time-consuming manual work, based on experience. Due to the use of new materials and production processes, it becomes more and more difficult to find the optimum process settings.
Therefore there is a strong need for an algorithm which is able to find the optimum settings for sheet forming processes. Nowadays, research on this topic is in full swing.

Anthology of finished graduate projects:

Industrial partners: