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 Liewald2018
%A Liewald, M
%A Karadogan, C
%A Lindemann, B
%A Jazdi, N
%A Weyrich, M
%D 2018
%I Elsevier
%J CIRP Journal of Manufacturing Science and Technology
%K Digital_manufacturing_system Forging IFU Knowledge_management ML from:jmueller
%P 116-120
%R 10.1016/j.cirpj.2018.04.002
%T On the tracking of individual workpieces in hot forging plants
%U https://linkinghub.elsevier.com/retrieve/pii/S1755581718300154
%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{Liewald2018,
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 = {2022-02-28T09:31:07.000+0100},
author = {Liewald, M and Karadogan, C and Lindemann, B and Jazdi, N and Weyrich, M},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2da1d2d738d22adb7f1d1af4ede10b4de/ifu},
doi = {10.1016/j.cirpj.2018.04.002},
file = {:U\:/IFU/Bibliothek/M Mendeley/Alexander Weiss/Liewald et al. - On the tracking of individual workpieces in hot forging plants - 2018.pdf:pdf},
interhash = {b0bd1051c74683aa4dc21959a0394d37},
intrahash = {da1d2d738d22adb7f1d1af4ede10b4de},
issn = {17555817},
journal = {CIRP Journal of Manufacturing Science and Technology},
keywords = {Digital_manufacturing_system Forging IFU Knowledge_management ML from:jmueller},
month = aug,
pages = {116-120},
publisher = {Elsevier},
timestamp = {2023-09-13T10:39:37.000+0200},
title = {On the tracking of individual workpieces in hot forging plants},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1755581718300154},
volume = 22,
year = 2018
}
