Data-driven Analysis of Product State Propagation in Manufacturing Systems Using Visual Analytics and Machine Learning

The importance of quality and efficiency has increased in recent years. Moreover, the rise of computational power and the development of advanced analytics has enabled the industry to enhance the performance of manufacturing systems. Therefore, further transparency of intermediate product states is...

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Bibliographic Details
Published in:Procedia CIRP Vol. 93; pp. 449 - 454
Main Authors: Filz, Marc-André, Gellrich, Sebastian, Herrmann, Christoph, Thiede, Sebastian
Format: Journal Article
Language:English
Published: Elsevier B.V 2020
Subjects:
Intermediate Product States
Machine Learning
Manufacturing Systems
Product Propagation
Product State Classes
Visual Analytics
Visual Analytics
Intermediate Product States
Product Propagation
Product State Classes
Machine Learning
Manufacturing Systems
ISSN:2212-8271, 2212-8271
Online Access:Get full text
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Summary:The importance of quality and efficiency has increased in recent years. Moreover, the rise of computational power and the development of advanced analytics has enabled the industry to enhance the performance of manufacturing systems. Therefore, further transparency of intermediate product states is necessary to derive appropriate actions. The goal of this paper is to develop a framework to enable the data-driven analysis of product state propagation within manufacturing systems to improve the transparency of product quality related cause-effect relationships. Based on their intermediate product features, machine learning algorithms assign products to classes of similar characteristics. This approach is practically applied to a case study from the electronic production industry. By using visual analytics tools, the propagation of product states along the manufacturing process chain is exemplarily analyzed.
ISSN:2212-8271
2212-8271
DOI:10.1016/j.procir.2020.03.065