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Material-based process-chain optimization in metal forming

: Morand, L.; Pagenkopf, J.; Helm, D.


Proceedings in applied mathematics and mechanics. PAMM 17 (2017), Nr.1, S.709-710
ISSN: 1617-7061
International Association of Applied Mathematics and Mechanics (GAMM Annual Meeting) <88, 2017, Weimar>
Zeitschriftenaufsatz, Konferenzbeitrag
Fraunhofer IWM ()
machine learning; neural network; metal forming; inverse problem

The characteristics of a manufacturing product are influenced by a variety of different factors, such as the material properties of the base product. The prediction of properties that give optimal results in metal forming applications is a complex task but of high interest for the manufacturer. To realize such a prediction scheme, the process chain is split up into individual process steps and for each of them an inverse modeling is required. The specific aim of this work is to present an approach for the inverse problem formulation of a process step and to solve it using methods of machine learning. Moreover, the challenges that often arise due to the ill-posed nature of inverse problems will be discussed. The main focus is on the crystallographic texture of metals, which strongly affect s the deformation behavior during a process step and highly influences the characteristics of the final product.