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

Approach to identify product and process state drivers in manufacturing systems using supervised machine learning

Veröffentlichungsdatum
2014-11-24
Autoren
Wuest, Thorsten  
Betreuer
Thoben, Klaus-Dieter  
Gutachter
Irgens, Christopher  
Zusammenfassung
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.
Schlagwörter
Manufacturing Systems

; 

Manufacturing Processes

; 

Product State

; 

Accumulating State Vector

; 

Quality

; 

Machine Learning

; 

Feature Selection

; 

Holistic Data and Information Management
Institution
Universität Bremen  
Fachbereich
Fachbereich 04: Produktionstechnik, Maschinenbau & Verfahrenstechnik (FB 04)  
Dokumenttyp
Dissertation
Zweitveröffentlichung
Nein
Sprache
Englisch
Dateien
Lade...
Vorschaubild
Name

00104199-1.pdf

Size

34.08 MB

Format

Adobe PDF

Checksum

(MD5):05fb8488df0cbf59c8ead123eb015239

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