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Model-based setup assistant for progressive tools

 
: Springer, R.; Gräler, M.; Homberg, W.; Henke, C.; Trächtler, A.

:

Fratini, Livan (Ed.) ; European Scientific Association for Material Forming:
21st International ESAFORM Conference on Material Forming, ESAFORM 2018. Proceedings : 23-25 April 2018, Palermo, Italy
Melville/NY: AIP Publishing, 2018 (AIP Conference Proceedings 1960)
ISBN: 978-0-7354-1663-5
Art.160027
International Conference on Material Forming (ESAFORM) <21, 2018, Palermo>
English
Conference Paper
Fraunhofer IEM ()

Abstract
In the field of production systems, globalization and technological progress lead to increasing requirements regarding part quality, delivery time and costs. Hence, today’s production is challenged much more than a few years ago: it has to be very flexible and produce economically small batch sizes to satisfy consumer’s demands and avoid unnecessary stock. Furthermore, a trend towards increasing functional integration continues to lead to an ongoing miniaturization of sheet metal components. In the industry of electric connectivity for example, the miniaturized connectors are manufactured by progressive tools, which are usually used for very large batches. These tools are installed in mechanical presses and then set up by a technician, who has to manually adjust a wide range of punch-bending operations. Disturbances like material thickness, temperatures, lubrication or tool wear complicate the setup procedure. In prospect of the increasing demand of production flexibility, this time-consuming process has to be handled more and more often. In this paper, a new approach for a model-based setup assistant is proposed as a solution, which is exemplarily applied in combination with a progressive tool. First, progressive tools, more specifically, their setup process is described and based on that, the challenges are pointed out. As a result, a systematic process to set up the machines is introduced. Following, the process is investigated with an FE-Analysis regarding the effects of the disturbances. In the next step, design of experiments is used to systematically develop a regression model of the system’s behaviour. This model is integrated within an optimization in order to calculate optimal machine parameters and the following necessary adjustment of the progressive tool due to the disturbances. Finally, the assistant is tested in a production environment and the results are discussed.

: http://publica.fraunhofer.de/documents/N-520408.html