Title: Enabling intelligent Mg-sheet processing utilizing efficient machine-learning algorithm
Authors: Shariati, Mohamadreza 
Weber, Wolfgang 
Bohlen, Jan
Kurz, Gerrit
Letzig, Dietmar
Höche, Daniel  
Language: en
Keywords: Universitätsbibliographie;Evaluation 2020
Issue Date: 9-Sep-2020
Publisher: Elsevier
Document Type: Article
Source: Enthalten in: Materials science & engineering. - Amsterdam : Elsevier, 1988. - Online-Ressource . - Bd. 792.2020, 139846
Journal / Series / Working Paper (HSU): Materials Science and Engineering A 
Volume: 794
Publisher Place: Amsterdam
Abstract: 
© 2020 Elsevier B.V. Process – property relationship control during magnesium sheet manufacturing is demanding due to the complexity of involved physical parameters and the sensitivity of the system to small changes. Here, data science might help to extract crucial information on interdependencies between processing parameters and sheet quality. In this paper we suggest a dedicated machine learning framework, which enables the possibility of correlating material property determining concepts such as pole figure to processing parameters, namely temperature and deformation degree without knowledge on prior dependencies of physical variables. Despite the impacts that using a relatively small data set can have, for Mg-AZ31 alloy we show that some projections of crystallographic texture can be reliably predicted from mechanical measurement data set. In general, the framework is useful for those processing parameters, which conventionally can be represented by a mathematical basis in the context of interpolation. In the future with access to more data it is proposed that applying our approach might allow predicting and controlling in-situ the rolling process route.
Organization Units (connected with the publication): Statik und Dynamik 
URL: https://ub.hsu-hh.de/DB=1/XMLPRS=N/PPN?PPN=1750133962
https://api.elsevier.com/content/abstract/scopus_id/85089593262
ISSN: 09215093
DOI: 10.1016/j.msea.2020.139846
Appears in Collections:2020

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