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Material‐based process‐chain optimization in metal forming
Author(s) -
Morand Lukas,
Pagenkopf Jan,
Helm Dirk
Publication year - 2017
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.201710323
Subject(s) - inverse , process (computing) , computer science , product (mathematics) , forming processes , chain (unit) , focus (optics) , variety (cybernetics) , industrial engineering , process engineering , work (physics) , mathematical optimization , mechanical engineering , mathematics , engineering , artificial intelligence , geometry , physics , astronomy , optics , operating system
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 affects the deformation behavior during a process step and highly influences the characteristics of the final product. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)