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: 40300 HUF
Availability: InStock

Szerző: Thorsten Wuest

Nyelv:

Kötés: Puha kötésű

Kiadó: Springer International Publishing AG

Elérhetőség: Beszállítói készleten alacsony példányszámban

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

Információk a könyvről

Szerző

Thorsten Wuest

Nyelv

Angol

Kötés

Könyv - Puha kötésű

Kiadva

2016

oldal

272

EAN

9783319386980

ISBN

3319386980

Enbook ID

15194632

Kiadó

Springer International Publishing AG

Súly

4453

Méretek

155 x 235 x 16

Teljes leírás

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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