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Citation link: https://nbn-resolving.de/urn:nbn:de:gbv:46-00104199-11
00104199-1.pdf
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Approach to identify product and process state drivers in manufacturing systems using supervised machine learning


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Other Titles: Ansatz zu Identifikation von relevanten Produkt- und Prozessparametern in Produktionssystemen durch den Einsatz von überwachtem maschinellen Lernen
Authors: Wuest, Thorsten 
Supervisor: Thoben, Klaus-Dieter
1. Expert: Thoben, Klaus-Dieter
Experts: Irgens, Christopher
Abstract: 
The developed concept allows identifying relevant state drivers of complex, multi-stage manufacturing systems holistically. It is able to utilize complex, diverse and high-dimensional data sets which often occur in manufacturing applications and integrate the important process intra- and inter-relations. The evaluation was conducted by using three different scenarios from distinctive manufacturing domains (aviation, chemical and semiconductor). The evaluation confirmed that it is possible to incorporate implicit process intra- and inter-relations on process as well as programme level through applying SVM based feature ranking. The analysis outcome presents a direct benefit for practitioners in form of the most important process parameters and state characteristics, so-called state drivers, of a manufacturing system. Given the increasing availability of data and information, this selection support can be directly utilized in, e.g., quality monitoring and advanced process control.
Keywords: Manufacturing Systems; Manufacturing Processes; Product State; Accumulating State Vector; Quality; Machine Learning; Feature Selection; Holistic Data and Information Management
Issue Date: 24-Nov-2014
Type: Dissertation
Secondary publication: no
URN: urn:nbn:de:gbv:46-00104199-11
Institution: Universität Bremen 
Faculty: Fachbereich 04: Produktionstechnik, Maschinenbau & Verfahrenstechnik (FB 04) 
Appears in Collections:Dissertationen

  

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