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

Name: Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning
Brand: Springer International Publishing AG
SKU: 9783319386980
Price: 425.21 PLN
Availability: InStock

Autor: Thorsten Wuest

Język:

Oprawa: Miękka

Wydawca: Springer International Publishing AG

Dostępność: Dostępna u dostawcy

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The book reports on a novel approach for holistically identifying the relevant state drivers of comp...

Informacje o książce

Autor

Thorsten Wuest

Język

Angielski

Oprawa

Książka - Miękka

Data wydania

2016

strony

272

EAN

9783319386980

ISBN

3319386980

Enbook ID

15194632

Wydawca

Springer International Publishing AG

Waga

4453

Wymiary

155 x 235 x 16

Pełny opis

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

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