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150508 ||| eng |
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|a 9783319176116
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| 100 |
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|a Wuest, Thorsten
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| 245 |
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|a Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning
|h Elektronische Ressource
|c by Thorsten Wuest
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| 250 |
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|a 1st ed. 2015
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2015, 2015
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| 300 |
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|a XVIII, 272 p. 139 illus., 10 illus. in color
|b online resource
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| 505 |
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|a Introduction -- Developments of manufacturing systems with a focus on product and process quality -- Current approaches with a focus on holistic information management in manufacturing -- Development of the product state concept -- Application of machine learning to identify state drivers -- Application of SVM to identify relevant state drivers -- Evaluation of the developed approach -- Recapitulation
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| 653 |
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|a Operations Management
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| 653 |
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|a Production management
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| 653 |
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|a Industrial engineering
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| 653 |
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|a Computer-Aided Engineering (CAD, CAE) and Design
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| 653 |
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|a Computational intelligence
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| 653 |
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|a Computational Intelligence
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| 653 |
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|a Industrial and Production Engineering
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| 653 |
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|a Computer-aided engineering
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| 653 |
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|a Production engineering
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
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|a Springer Theses, Recognizing Outstanding Ph.D. Research
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| 028 |
5 |
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|a 10.1007/978-3-319-17611-6
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| 856 |
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|u https://doi.org/10.1007/978-3-319-17611-6?nosfx=y
|x Verlag
|3 Volltext
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|a 670
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| 520 |
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|a The book reports on a novel approach for holistically identifying the relevant state drivers of complex, multi-stage manufacturing systems. This approach is able to utilize complex, diverse and high-dimensional data sets, which often occur in manufacturing applications, and to integrate the important process intra- and interrelations. The approach has been evaluated using three scenarios from different manufacturing domains (aviation, chemical and semiconductor). The results, which are reported in detail in this book, confirmed that it is possible to incorporate implicit process intra- and interrelations on both a process and programme level by applying SVM-based feature ranking. In practice, this method can be used to identify the most important process parameters and state characteristics, the 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. Importantly, the method is neither limited to specific products, manufacturing processes or systems, nor by specific quality concepts
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