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Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning



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Éditeur :

Springer


Collection :

Springer Theses

Paru le : 2015-04-20



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Description

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.
Pages
272 pages
Collection
Springer Theses
Parution
2015-04-20
Marque
Springer
EAN papier
9783319176109
EAN EPUB
9783319176116
Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
27
Taille du fichier
6420 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
94,94 €

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