Approach to identify product and process state drivers in manufacturing systems using supervised machine learning
Date Issued
2014-11-24
Author(s)
Supervisors
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.
Subjects
Manufacturing Systems
;
Manufacturing Processes
;
Product State
;
Accumulating State Vector
;
Quality
;
Machine Learning
;
Feature Selection
;
Holistic Data and Information Management
Institution
Type
Dissertation
Secondary publication
No
Language
English
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00104199-1.pdf
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