Tracking each workpiece provides two major advantages in forging technology. First, the matching of physical workpiece with the monitored process
information facilitates root‐cause analysis for product quality. Second, the following process steps can be adapted according to the incoming workpiece
properties to improve the robustness of hot forging process chain. The paper presents a general tracking methodology and tagging experiments on
aluminium and steel forgings for harsh drop‐forging technology. Furthermore, a framework for streaming and processing large amounts of real‐time
data as well as a multidimensional approach to model and analyse the workpiece information for individual and batch‐tracking are presented.
%0 Journal Article
%1 Liewald_2018
%A Liewald, Mathias
%A Karadogan, Celalettin
%A Lindemann, Benjamin
%A Jazdi, Nasser
%A Weyrich, Michael
%D 2018
%I Elsevier
%J CIRP Journal of Manufacturing Science and Technology
%K 2018applikation bigdataanalytics ias
%P 116 - 120
%R 10.1016/j.cirpj.2018.04.002
%T On the tracking of individual workpieces in hot forging plants
%U https://www.ias.uni-stuttgart.de/dokumente/publikationen/2018_On_the_tracking_of_individual_workpieces_in_hot_forging_plants.pdf
%V 22
%X Tracking each workpiece provides two major advantages in forging technology. First, the matching of physical workpiece with the monitored process
information facilitates root‐cause analysis for product quality. Second, the following process steps can be adapted according to the incoming workpiece
properties to improve the robustness of hot forging process chain. The paper presents a general tracking methodology and tagging experiments on
aluminium and steel forgings for harsh drop‐forging technology. Furthermore, a framework for streaming and processing large amounts of real‐time
data as well as a multidimensional approach to model and analyse the workpiece information for individual and batch‐tracking are presented.
@article{Liewald_2018,
abstract = {Tracking each workpiece provides two major advantages in forging technology. First, the matching of physical workpiece with the monitored process
information facilitates root‐cause analysis for product quality. Second, the following process steps can be adapted according to the incoming workpiece
properties to improve the robustness of hot forging process chain. The paper presents a general tracking methodology and tagging experiments on
aluminium and steel forgings for harsh drop‐forging technology. Furthermore, a framework for streaming and processing large amounts of real‐time
data as well as a multidimensional approach to model and analyse the workpiece information for individual and batch‐tracking are presented.},
added-at = {2019-12-13T17:21:57.000+0100},
author = {Liewald, Mathias and Karadogan, Celalettin and Lindemann, Benjamin and Jazdi, Nasser and Weyrich, Michael},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2a274f178230e16991789b4d091a99e5f/taylansngerli},
doi = {10.1016/j.cirpj.2018.04.002},
interhash = {b0bd1051c74683aa4dc21959a0394d37},
intrahash = {a274f178230e16991789b4d091a99e5f},
journal = {CIRP Journal of Manufacturing Science and Technology},
keywords = {2018applikation bigdataanalytics ias},
language = {eng},
month = {August},
pages = {116 - 120},
publisher = {Elsevier},
timestamp = {2020-04-20T10:21:37.000+0200},
title = {On the tracking of individual workpieces in hot forging plants},
url = {https://www.ias.uni-stuttgart.de/dokumente/publikationen/2018_On_the_tracking_of_individual_workpieces_in_hot_forging_plants.pdf},
volume = 22,
year = 2018
}