The growing globalization leads to an increasing demand on competition between industrial companies. The manufacturing processes using innovative operations and advanced materials make it possible to develop a competitive power with a high level productivity. In this paper the process of rotary blanking is introduced as an innovative manufacturing method. It enables users to work more efficient and to reduce product costs with a high process speed. In order to understand this modern manufacturing technology, it is needed to analyze process parameters. In particular it is necessary to determine which materials are preferable for the rotary blanking. Therefore, the influence of different tool materials on the part quality was investigated through experiments. Experimental results of the rotary blanking from different tool materials are discussed and they are compared with the results of finite element simulations in this paper.

In this paper, we propose a new approach for the simulation-based support of tryout operations in deep drawing which can be schematically classified as automatic knowledge acquisition. The central idea is to identify information maximising sensor positions for draw-in as well as local blank holder force sensors by solving the column subset selection problem with respect to the sensor sensitivities. Inverse surrogate models are then trained using the selected sensor signals as predictors and the material and process parameters as targets. The final models are able to observe the drawing process by estimating current material and process parameters, which can then be compared to the target values to identify process corrections. The methodology is examined on an Audi A8L side panel frame using a set of 635 simulations, where 20 out of 21 material and process parameters can be estimated with an R2 value greater than 0.9. The result shows that the observational models are not only capable of estimating all but one process parameters with high accuracy, but also allow the determination of material parameters at the same time. Since no assumptions are made about the type of process, sensors, material or process parameters, the methodology proposed can also be applied to other manufacturing processes and use cases.

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The increasing individualization of products assigns manufacturing companies to new tasks like manufacturing various products in a more efficient way. This progression in the market leads on the one hand to a new product design and on the other hand to an improved production process. Both are necessary to reduce assembly, service and recycling costs. Hence the joining technology is and will become more and more important. The conventional joining technologies like welding, bonding, bolting or clamping have their own disadvantages especially in the field of flexibility. In order to reduce the effort for assembling and disassembling by retaining the requirements of the connection a new innovative joining technology is needed. In this study a new joining technology is introduced to become faster and more flexible in assembling and disassembling. The basic idea of this manufacturing technology comes from a “metal hook and loop fastener”. A hook and loop fastener consisting of metal has a lot of advantages for the fields of industrial assembly, service and recycling. Similar to the synthetic hook and loop fastener a metal one is characterized by easy closing and opening without special tools. And in comparison to the synthetic hook and loop fastener the transmissible forces are very high. An additional benefit can be gained for instance in shock absorbing or resistance against chemical and thermal influence. Two solutions are followed up to invent the “metal hook and loop fastener”. A one-to-one copy of the conventional hook and loop fastener is constructed in metal and specific solutions for the use of metal are tested. A conventional finite element program was used in order to optimize the construction of a metal cocklebur and the results show a good agreement with the experiment.

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